Countries with lower literacy levels need different COVID-19 communication strategies

People have a right to access the information they need during the COVID-19 pandemic. But the format and language of that information need to evolve as COVID-19 spreads to nations with lower literacy rates and more vulnerable groups of people.

covid-19 literacy rates communication strategies

The information should be easy for people to find, understand and use. It’s unwise to assume that written formats are always the most efficient way to convey information. As the disease rapidly expands into countries with lower rates of literacy, organizations involved in the response need to shift focus from written information to developing significantly more pictorial, audio, and video content. 

That is the best way to ensure that older people, women, and other vulnerable people in those countries have the best chance of understanding lifesaving information. 

It’s also a necessary adjustment where infection control limits in-person community engagement. Social media, SMS services, call centers, television, and radio will be essential communication channels. Formats need to diversify accordingly if the message is to get across.

Literacy dynamics are rapidly changing

COVID-19 is now rapidly spreading in countries with lower literacy rates. The average literacy rate in countries with confirmed COVID-19 cases on February 19 was 94%. One month later it was 89%.

The highest rates of change in new COVID-19 cases being recorded are predominantly in countries with lower literacy rates. Between March 16 and 22, the 15 countries with the highest percentage change of new COVID-19 cases had an average literacy rate of 85%. These include countries like Cambodia, Cameroon, Côte d’Ivoire, DRC, Nigeria, Tanzania, and Togo. All these countries saw increases in confirmed cases of at least 900% during this seven-day period.

Women’s literacy rates are often lower than men’s

In countries where UNESCO measures literacy, average literacy rates are 6% higher for men than for women. For example in Yemen, 73% of men and only 35% of women above the age of 15 can read or write a basic sentence about their life, a difference of 38%. The gender difference is also stark in Pakistan (25%), DRC (23%) and Mali (20%). 

The map below highlights the gender difference in adult literacy in individual countries. Orange shading indicates countries where male literacy rates are higher than female literacy rates. Blue shading indicates the few countries where female literacy rates are higher than male literacy rates. 

Older people often have lower literacy rates than people under 65 

In many countries, older people are less likely to be able to read than younger adults. This limits their ability to access written information on COVID-19.

The average elderly literacy rate in countries UNESCO reports literacy data for, is 65%. UNESCO defines elderly people as those aged 65 or older. In countries with documented literacy rates from the same year, people aged between 15 and 64 have an average literacy rate 19% higher than people 65 years or older. The difference is greatest in Libya (63%), Timor-Leste (53%), Cabo Verde (50%), and Iran (49%).

Use data to design more inclusive communication strategies

To design effective COVID-19 communication strategies, responders need reliable data about language and literacy. As part of our COVID-19 response, we are making the necessary data openly available.

This is part of a Translators without Borders initiative to help make targeted information strategies more data driven. Language and literacy maps and datasets exist for DRC, Guatemala, Malawi, Mozambique, Nigeria, Pakistan, the Philippines, Ukraine, and Zambia. 

Along with these existing maps and the interactive global literacy map above, we are also scaling up our efforts to release more subnational language and literacy data for countries affected by the COVID-19 pandemic. This week we released national and sub-national data for Thailand. We will release more datasets and data visualizations over the next few weeks and months, so stay tuned to our COVID-19 webpage or the Humanitarian Data Exchange for updates.

We derived most of those datasets from historical census data, typically available down to the Admin 2 (district or county) level. Such data is most useful when it is analyzed alongside up-to-date information on language and communication needs. To help us with our ongoing language data initiative, we urge organizations to include four simple language questions in needs assessments and surveys related to COVID-19.

Make content available in multiple formats

Organizations responding to the pandemic should use improved data to develop communication strategies that are geared to the needs of the target population. Preparedness is a critical component of this. Organizations should develop content in as many formats as possible, recognizing that pictorial, audio, and video content is easier to access and absorb for many people. Additionally, older people often benefit from content that is easier to read. This requires incorporating design considerations such as larger fonts and good contrast. Plain-language principles also offer a useful model for creating clear and concise written and verbal content. The WHO proposes several key principles for improving understanding of health content.

In the rapidly evolving context of the COVID-19 response, organizations should complement written information with other formats. This is vital to ensure information is both believed and understood. We need to do this early to ensure people living in places with lower literacy levels don’t receive information too late to make a difference. 

Written by Eric DeLuca, Monitoring, Evaluation, and Learning Manager, Translators without Borders

The project is funded by the H2H Fund, a funding mechanism for H2H Network members. The fund is a rapid funding vehicle for network members responding to humanitarian crises.

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In the Democratic Republic of Congo:

Communicating in the languages of affected people is a priority for the latest Ebola response plan, and beyond

On 2 March, the authorities in the Democratic Republic of Congo announced that the last Ebola patient had been discharged from a treatment center. The epidemic isn’t over yet. But, after 18 months during which more than 3,400 people have been infected and over 2,250 died, the relief is palpable. Looking ahead, the Congolese government and its humanitarian partners turn their attention to implementing lessons from this 10th Ebola outbreak. In a country where more than 200 languages are spoken, prioritizing communication in the languages of affected people is one key lesson to help address the next emergency faster. The latest Ebola strategic response plan (SRP 4.1) points the way.

DRC Ebola response plan

The languages of affected people are finally a priority

The plan highlights the importance of improving risk communication and community engagement by using the languages and the formats preferred by people at risk. This includes developing communication tools and feedback mechanisms in appropriate languages, formats, and channels. The plan also emphasizes the need to equip health communicators to relay accurate information in local languages and with culturally acceptable wording.

For the first time since the beginning of this outbreak, the SRP mentions these issues. This is a key advance in adopting insights highlighted by health professionals, anthropologists, and communication specialists. It addresses three key factors TWB identified as critical to the effectiveness of Ebola-related communication: the languages that responders use; the content that responders deliver; and the way responders deliver that content. It also acknowledges the importance of feedback gathered from affected people by linking it to follow-up actions. People continue to have concerns and questions around Ebola and response efforts. Their concerns must be heard and their questions answered as the current outbreak draws to a close.

This is an important lesson that matters beyond Ebola

In multilingual DRC, to help people protect themselves responders need to listen, understand, and provide information and services in the languages of those at risk. Improving communication cannot alone guarantee better outcomes. But unless language is built into risk communication and community engagement strategies, response teams are unlikely to be effective.

Ebola DRC response plan

Recent actions by the risk communication and community engagement working group provide a case in point. Rumors and confusion have impeded efforts to contain the outbreak. So the group developed a multilingual tool to address the 25 most frequently asked questions. They collected the questions through the response-wide community feedback mechanism. The group members jointly drafted answers, and TWB supported with plain language editing to ensure accuracy and clarity. The group involved Ebola survivors to ensure the wording did not stigmatize them. Questions and answers were then translated into local languages for the widest possible reach and understanding. This tool equips responders to prevent the spread of misinformation and keep people safe.

Health professionals, social researchers, communication experts, and affected people worked together to provide and disseminate accurate, understandable information. This should be standard practice in mitigating the consequences of this outbreak, preparing for future health emergencies, and addressing wider humanitarian needs.

It is high time to turn evidence into action

The Congolese government and its humanitarian partners have a crucial role to play in implementing the latest response plan. And it seems they finally intend to give affected people’s languages and communication preferences the attention they deserve. TWB will work closely with those who are committed to a more language-aware approach. By proactively developing field teams’ capacity and resources, we can lift the language barriers to effective and accountable risk communication and community engagement.

Written by Mia Marzotto, Senior Advocacy Officer and Laure Venier, Community Engagement Program Coordinator for DRC, Translators without Borders.

Valérie travels the world and translates

Translators improve lives by translating potentially lifesaving information into languages spoken by vulnerable individuals. Those who volunteer as part of the Translators without Borders (TWB) Community have a range of experiences and skills. They share our vision of a world where knowledge knows no language barriers. We are grateful for all our translators, and we love sharing their stories.

Valérie Thirkettle is a multi-talented translator who has worked with TWB since 2018 and has donated almost 550,000 words of life-saving information. Her dedication and motivation to take on new projects and the care she puts into her translations make it an absolute pleasure to collaborate. Valérie is a lawyer who spent the majority of her career working for a prestigious intergovernmental organization dedicated to the exploration of space. Recently, she retired to pursue her passion for translation.

Valérie travels and translates
“How I feel when I sit down to face a big revision task” – Valérie.

A flexible working life 

An avid traveler who divides her time between the Netherlands and South Africa, she enjoys the flexibility of TWB’s internet-based system. It gives her the chance to enjoy her other pleasures, studying literary translation, spending time with family and friends, golfing and enjoying nature, particularly in her beloved Africa. All the while, wherever she goes she can feed what she calls her “translation addiction.”

Valérie in Africa
Valérie enjoys the natural surroundings of Africa.

“I was attracted by TWB’s technology focus. I discovered how much language matters in humanitarian settings, so I hope my contribution can help people. And that it can improve the advocacy efforts of the organizations I translate for.”

Her ability to infuse her multi-sector knowledge into her translation work allows her to work on a number of different projects. “I am a trained lawyer and I have worked in international legal subjects and HR subjects. I like to make myself useful with the skills I have and contribute to the causes that resonate with me, and on a volunteer basis.” 

Valerie keeps in contact with TWB’s Language Services Team by email. She is celebrated as a central, fun member of the community. The team recalls sharing many laughs with Valerie. With her varied experience, Valérie has seen the funny side of translation and mistranslation. She told us a story about a translation she once reviewed in which  she noticed the section to sign and “date” the form mistakenly read “rendez vous d’amour.” “I loved it,” laughed Valerie, “filling in forms suddenly turned into something really exciting!”

Education for everyone

One of her favorite projects with TWB involved the revision and final linguistic sign-off of the Communicating with Disaster Affected Communities (CDAC) Network’s How-to Guide to Collective Communication and Community Engagement. This is essential for teaching better communication strategies on the ground. It helps inform people about their rights and situations in languages they understand. 

Translators can often become emotionally involved in a project. When working with Street Child, for instance, Valérie says, 

“I felt a strong resonance with the task, and, like with a good novel, the end came too early!” 

Children in Bangladesh
Children learning in school, Bangladesh.

In fact, projects that assist young people tend to stand out for Valérie. Her time working with Think Equal also left an impactful and memorable mark. Think Equal has developed an early years education program for social and emotional learning. It was a large project in which Valérie took care of the entire revision. It included revising French versions of the program, an extensive set of books, lesson plans, and teaching materials. “The size and spread of this project made it complex, but an opportunity to develop new organizational skills for my translations.” 

Overall, her translation experience has taught Valérie to appreciate the varied skills of other translators. She comments on how they build on one another’s strengths to deliver great work. She’s become increasingly involved in revising tasks and has embarked on qualifications in revising and proofreading. “My work with TWB gives me a great opportunity for continuous learning.”

One of her tips for other Kató translators is to “pay attention to the glossaries and be as consistent as possible with the terminology you use.” Valérie points out that you’re able to ask project managers for feedback throughout the process. “And of course, keep claiming more tasks, the humanitarian sector needs all the language help it can get!” 

Get involved with the TWB translator community.

Written by Danielle Moore, Communications Officer for Translators without Borders. Interview responses by Valérie Thirkettle, Translator for Translators without Borders.

Language data fills a critical gap for humanitarians

Until now, humanitarians have not had access to data about the languages people speak. But a series of open-source language datasets is about to improve how we communicate with communities in crisis. Eric DeLuca and William Low explain how a seemingly simple question drove an innovative solution.

“Do you know what languages these new migrants speak?”

Lucia, an aid worker based in Italy, asked this seemingly simple question to researchers from Translators without Borders in 2017. Her organization was providing rapid assistance to migrants as they arrived at the port in Sicily. Lucia and her colleagues were struggling to provide appropriate language support. They often lacked interpreters who spoke the right languages and they asked migrants to fill out forms in languages that the migrants didn’t understand.

Unfortunately, there wasn’t a simple answer to Lucia’s question. In the six months prior to our conversation with Lucia, Italy registered migrants from 21 different countries. Even when we knew that people came from a particular region in one of these countries, there was no simple way to know what language they were likely to speak.

The problem wasn’t exclusive to the European refugee response. Translators without Borders partners with organizations around the world which struggle with a similar lack of basic language data.

Where is the data?

As we searched various linguistic and humanitarian resources, we were convinced that we were missing something. Surely there was a global language map? Or at least language data for individual countries?

The more we looked, the more we discovered how much we didn’t know. The language data that does exist is often protected by restrictive copyrights or locked behind paywalls. Languages are often visualized as discrete polygons or specific points on a map, which seems at odds with the messy spatial dynamics that we experience in the real world. 

In short, language data isn’t accessible, or easily verifiable, or in a format that humanitarians can readily use.

We are releasing language datasets for nine countries

Today we launch the first openly available language datasets for humanitarian use. This includes a series of static and dynamic maps and 23 datasets covering nine countries: DRC, Guatemala, Malawi, Mozambique, Nigeria, Pakistan, Philippines, Ukraine, and Zambia.

This work is based on a partnership between TWB and University College London. The pilot project received support from Research England’s Higher Education Innovation Fund, managed by UCL Innovation & Enterprise. With support from the Centre for Translation Studies at UCL, this project was the first of its kind in the world to systematically gather and share language data for humanitarian use.

The majority of these datasets are based on existing sources — census and other government data. We curated, cleaned, and reformatted the data to be more accessible for humanitarian purposes. We are exploring ways of deriving new language data in countries without existing sources, and extracting language information from digital sources.

This project is built on four main principles:

TWB Language Data Initiative

1. Language data should be easily accessible

We started analyzing existing government data because we realized there was a lot of quality information that was simply hard to access and analyze. The language indicators from the 2010 Philippines census, for example, were spread over 87 different spreadsheets. Many census bureaus also publish in languages other than English, making it difficult for humanitarians who work primarily in English to access the data. We have gone through the process of curating, translating, and cleaning these datasets to make them more accessible.

2. Language data should work across different platforms

We believe that data interoperability is important. That is, it should be easy to share and use data across different humanitarian systems. This requires data to be formatted in a consistent way and spatial parameters to be well documented. As much as possible, we applied a consistent geographic standard to these datasets. We avoided polygons and GPS points, opting instead to use OCHA administrative units and P-codes. At times this will reduce data precision, but it should make it easier to integrate the datasets into existing humanitarian workflows.

We worked with the Centre for Humanitarian Data to develop and apply consistent standards for coding. We built an HXL hashtag scheme to help simplify integration and processing. Language standardization was one of the most difficult aspects of the project, as governments do not always refer to languages consistently. The Malawi dataset, for example, distinguishes between “Chewa” and “Nyanja,” which are two different names for the same language. In some cases, we merged duplicate language names. In others, we left the discrepancies as they exist in the original dataset and made a note in the metadata.

Even when language names are consistent, the spelling isn’t always. In the DRC dataset, “Kiswahili” is displayed with its Bantu prefix. We have opted instead to use the more common English reference of “Swahili.”

Every dataset uses ISO 639-3 language codes and provides alternative names and spellings to alleviate some of the typical frustrations associated with inconsistent language references.

3. Language data should be open and free to use

We have made all of these datasets available under a Creative Commons Attribution Noncommercial Share Alike license (CC BY-NC-SA-4.0). This means that you are free to use and adapt them as long as you cite the source and do not use them for commercial purposes. You can also share derivatives of the data as long as you comply with the same license when doing so.

The datasets are all available in .xlsx and .csv formats on HDX, and detailed metadata clearly states the source of each dataset along with known limitations. 

Importantly, everything is free to access and use.

4. Language data should not increase people’s vulnerability

Humanitarians often cite the potential sensitivities of language as the primary reason for not sharing language data. In many cases, language can be used as a proxy indicator for ethnicity. In some, the two factors are interchangeable.

As a result, we developed a thorough risk-review process for each dataset. This identifies specific risks associated with the data, which we can then mitigate. It also helps us to understand the potential benefits. Ultimately, we have to balance the benefits and risks of sharing the data. Sharing data helps humanitarian organizations and others to develop communication strategies that address the needs of minority language speakers.

In most cases, we aggregated the data to protect individuals or vulnerable groups. For each dataset, we describe the method we used to collect and clean the data, and specify potential imitations. In a few instances, we chose to not publish datasets at all.

How can you help?

This is just the beginning of our effort to provide more accessible language data for humanitarian purposes. Our goal is to make language data openly available for every humanitarian crisis, and we can’t do it alone. We need your help to:

  1. Integrate and share this data. We are not looking to create another data portal. Our strategy is to make these datasets as accessible and interoperable as possible using existing platforms. But we need your feedback so we can improve and expand them.
  2. Add language-related questions into your ongoing surveys. Existing language data is often outdated and does not necessarily represent large-scale population movements. Over the past year, we have worked with partners such as IOM DTM, REACH, WFP, and UNICEF to integrate standard language questions into ongoing surveys. This is essential if we are to develop language data for the countries that don’t have regular censuses. The recent multi-sectoral needs assessment in Nigeria is a good example of how a few strategic language questions can lead to data-driven humanitarian decisions.
  3. Use this language data to improve humanitarian communication strategies. As we develop more data, we hope to provide the tools for Lucia and other humanitarians to design more appropriate communication strategies. Decisions to hire interpreters and field workers, develop radio messaging, or create new posters and flyers should all be data-driven. That’s only possible if we know which languages people speak. An inclusive and participatory humanitarian system requires two-way communication strategies that use languages and formats that people understand.

Clearly, the answer to Lucia’s question turned out to be more complicated than any of us expected. This partnership between TWB and the Centre for Translation Studies at UCL has finally made it possible to incorporate language data into humanitarian workflows. We have established a consistent format, an HXL coding scheme, and processes for standardizing language references. But the work does not stop with these nine countries. Over the next few months we will continue to curate and share existing language datasets for new countries. In the longer term we will be working with various partners to collect and share language data where it does not currently exist. We believe in a world where knowledge knows no language barriers. Putting language on the map is the first step to achieving that.

Eric DeLuca is the Monitoring, Evaluation, and Learning Manager at Translators without Borders.

William Low is a Senior Data and GIS Researcher at University College London.

Funding for this project was provided by Research England’s Higher Education Innovation Fund, managed by UCL Innovation & Enterprise.

TWB intern is recognized as a Young African Leader

Cédrick Young African Leader YALI
Cédrick Irakoze

At Translators without Borders (TWB), we are lucky to have extraordinary team members who are recognized worldwide. We are always grateful to have uniquely skilled members of the international community choose to be part of our cause. Today, we are proud to share the story of Cédrick Irakoze, Crisis Response and Community and Recruitment Intern for TWB. He was recently awarded a place to be part of the Young African Leaders Initiative (YALI) network. The YALI network invests in the next generation of African leaders, providing invaluable opportunities to connect and learn from experts. Learn more about the YALI network here.

Cédrick is a young Burundian language professional. He holds a bachelor’s degree in TESOL  (Teaching English to Speakers of Other Languages) from the University of Burundi, and has years of experience as a professional translator. He believes that language can improve or even save lives in this global world. And interaction in the right language can be vital for everyone, no matter people’s language, culture, or the color of their skin.

“TWB is my professional home” – Cédrick

In 2018, Cédrick first featured in our blog as a volunteer translator from English and French into Rundi. This was his introduction to the world of language in humanitarian work: “When I joined TWB as an intern, I joined a community of like-minded individuals serving the global community. Now I call TWB my professional home.” Day-to-day, Cédrick engages and collaborates with our translator community to help create a world with no language barriers. 

But in late 2019, he did something different. He successfully applied for the Young African Leaders Initiative program.

The Young African Leaders Initiative (YALI)

In 2016, Cédrick joined the Young African Leaders Initiative network with over 25,000 other young and talented individuals. In 2019, he met with 108 successful candidates from over 7000 applicants to attend a one-month leadership training course.

A group of YALI network members in Nairobi.
A group of YALI network members in Nairobi.

 

Energetic public officials, business owners, and local and international nonprofit leaders from all over Africa came together in Nairobi, Kenya. On hearing their stories, Cédrick reflected, “The way they are each committed to making their communities better inspired me.” The Translators without Borders team is delighted to have witnessed a team member take on such an exciting, formative challenge.

“Thank you very much. TWB showed me so much love and support before and during the program!” – Cédrick

Cédrick Irakoze, right, with TWB Kenya Manager Paul Warambo, left.
Cédrick Irakoze, right, with TWB Kenya Manager Paul Warambo, left.

It’s all about communication

The course was about inspiring and equipping one another to become better leaders. Participants developed their communication skills and built solutions-oriented networks. These factors are central to the changes these young leaders want to see in society. Each member of the diverse group – native speakers of over fifty languages – played a vital part.

Cédrick Irakoze, left presenting to the YALI network members.

This richness and diversity are reflected in TWB’s own community of translators and supporters, and in our way of working. We too rely on the power of teamwork to make change — to improve communications and access to information worldwide. Cédrick’s big takeaway is that when we come together we can innovate, we can flourish and we can make each other feel valued. 

“Diversity is richness in professional life” – Cédrick 

With the skills he’s learned through this course, Cédrick hopes to make a positive impact in his professional and social circles. “I can’t wait to contribute more and better to our common mission: to create a world that knows no language barriers.” 

Cédrick Irakoze and friends at the YALI network meetup in Nairobi.
Cédrick Irakoze and friends at the YALI network meetup in Nairobi.

 

Start your own journey as part of the TWB community.

 

Written by Danielle Moore, Communications Officer for Translators without Borders. Interview responses by Cédrick Irakoze, Crisis Response and Community and Recruitment Intern for Translators without Borders.

Humanitarian work close to home: Irina Nosova 

Translators improve lives by translating potentially lifesaving information into languages spoken by vulnerable individuals. Those who volunteer as part of the Translators without Borders (TWB) Community have a range of experiences and skills. They share our vision of a world where knowledge knows no language barriers. We are grateful for all our translators, and we love sharing their stories.

Shared philosophies

Irina’s philosophy fits right in with that of Translators without Borders: “In our information-packed society, it is essential to maintain access to vital information for everybody. So, my biggest motivation is helping people by delivering information to interested parties,” says Irina, former lawyer now turned English-Russian translator. Since joining as a volunteer translator in 2016, Irina has translated and revised a total of 110,220 words into Russian, one of our top ten most frequently requested languages.

Irina Nosova English Russian translator
Irina Nosova, translator

The projects touch on all sorts of information, including healthcare. Often, information and research in one language benefit speakers of another language – but it needs to be accurate and it needs to be available. So we asked about a particular project which made vital information available in Irina’s own country, in Russian, her mother-tongue. When tasked with translating an important anti-tuberculosis study she found it to be one of her most difficult projects to date. The translator translated study protocol and presentations to find out more, before later reading news articles and discovering the reality of the tuberculosis situation in Russia.

“I was shocked to find how high the burden of tuberculosis is in my country!” – Irina

Finding practical solutions

Although Irina focuses on the importance of language in Russia, she is also hopeful about how sharing information in many languages can spread helpful and life-changing information. “I hope that the study of novel tuberculosis treatment will speed up the registration of new drugs which are vital for successful treatment,” she told TWB. 

As well as more academic pieces, Irina finds translating personal stories equally important. One of her projects involved translating patients’ stories for partner non-profit EURORDIS – Rare Diseases Europe. 

“I realized that stories shared by patients with rare diseases and their families could inform people in similar situations in Russia about how to deal with those diseases.” – Irina 

Tapping into your skillset

Irina’s desire to help as a volunteer translator has helped her tap into personal and professional skills. “Volunteering improved my time management: I have to calculate and allocate the time I can spend to complete the tasks before the deadline, alongside my other daily tasks. Before launching my own business, I worked as a lawyer and I volunteered with TWB at night, after work. Now, I can be more flexible and am able to contribute more time.”

Her advice for other TWB Community members is to constantly improve your skills, learn new terminology, and check your quality of translation. “Doing translation in Kató – TWB’s online translation environment – requires the same quality approach as any other project: the highest possible. So, before claiming the new task make sure you understand the topic and do your own research to provide the best possible translation.” It helps you understand the context and importance of the situation you are translating about – like in the case of Irina’s tuberculosis project. When Irina dug deeper into the topic she was translating about, she discovered a personal interest in medical translation and later, clinical research. Her projects opened new doors: “Volunteering with TWB improved my resourcefulness and research skills and pushed me to explore new horizons in translation, take new courses, and dig deeper.” 

Join our translator community.

Written by Gloria Malone and Danielle Moore, Communications Officers for Translators without Borders. Interview responses by Irina Nosova.

Transfer Learning Approaches for Machine Translation

This article was originally posted in the TWB Tech Blog on medium.com

TWB’s current research focuses on bringing language technology to marginalized communities

Translators without Borders (TWB) aims to empower people through access to critical information and two-way communication in their own language. We believe language technology such as machine translation systems are essential to achieving this. This is a challenging task given many of the languages we work with have little to no language data available to build such systems.

In this post, I’ll explain some methods for dealing with low-resource languages. I’ll also report on our experiments in obtaining a Tigrinya-English neural machine translation (NMT) model.

The progress in machine translation (MT) has reached many remarkable milestones over the last few years, and it is likely that it will progress further. However, the development of MT technology has mainly benefited a small number of languages.

Building an MT system relies on the availability of parallel data. The more present a language is digitally, the higher the probability of collecting large parallel corpora which are needed to train these types of systems. However, most languages do not have the amount of written resources that English, German, French and a few other languages spoken in highly developed countries have. The lack of written resources in other languages drastically increases the difficulty of bringing MT services to speakers of these languages.

Low-resource MT scenario

Figure 2, modified from Koehn and Knowles (2017), shows the relationship between the BLEU score and the corpus size for the three MT approaches.

A classic phrase-based MT model outperforms NMT for smaller training set sizes. Only after a corpus size threshold of 15M words, roughly equivalent to 1 million sentence pairs, classic NMT shows its superiority.

Low-resource MT, on the other hand, deals with corpus sizes that are around a couple of thousand sentences. Although this figure shows at first glance that there is no way to obtain anything useful for low resource languages, there are ways to leverage even small data sets. One of these is a deep learning technique called transfer learning, which makes use of the knowledge gained while solving one problem to apply it to a different but related problem.

Cross-lingual transfer learning

Figure 3 illustrates their idea of cross-lingual transfer learning.

The researchers first trained an NMT model on a large parallel corpus — French–English — to create what they call the parent model. In a second stage, they continued to train this model, but fed it with a considerably smaller parallel corpus of a low-resource language. The resulting child model inherits the knowledge from the parent model by reusing its parameters. Compared to a classic approach of training only on the low-resource language, they record an average improvement of 5.6% BLEU over the four languages they experiment with. They further show that the child model doesn’t only reuse knowledge of the structure of the high resource target language but also on the process of translation itself.

The high-resource language to choose as the parent source language is a key parameter in this approach. This decision is usually made in a heuristic way judging by the closeness to the target language in terms of distance in the language family tree or shared linguistic properties. A more sound exploration of which language is best to go for a given language is made in Lin et al. (2019).

Multilingual training

What results from the example is one single model that translates from the four languages (French, Spanish, Portuguese and Italian) to English.

Multilingual NMT offers three main advantages. Firstly, it reduces the number of individual training processes needed to one, yet the resulting model can translate many languages at once. Secondly, transfer learning makes it possible for all languages to benefit from each other through the transfer of knowledge. And finally, the model serves as a more solid starting point for a possible low-resource language.

For instance, if we were interested in training MT for Galician, a low-resource romance language, the model illustrated in Figure 4 would be a perfect fit as it already knows how to translate well in four other high-resource romance languages.

A solid report on the use of multilingual models is given by Neubig and Hu (2018). They use a “massively multilingual” corpus of 58 languages to leverage MT for four low-resource languages: Azeri, Belarusian, Galician, and Slovakian. With a parallel corpus size of only 4500 sentences for Galician, they achieved a BLEU score of up to 29.1% in contrast to 22.3% and 16.2% obtained with a classic single-language training with statistical machine translation (SMT) and NMT respectively.

Transfer learning also enables what is called a zero-shot translation, when no training data is available for the language of interest. For Galician, the authors report a BLEU score of 15.5% on their test set without the model seeing any Galician sentences before.

Case of Tigrinya NMT

Tigrinya is no longer in the very low-resource category thanks to the recently released JW300 dataset by Agic and Vulic. Nevertheless, we wanted to see if a higher resource language could help build a Tigrinya-to-English machine translation model. We used Amharic as a parent language, which is written with the same Ge’ez script as Tigrinya and has larger public data available.

The datasets that were available to us at the time of writing this post are listed below. After JW300 dataset, the largest resource to be found is Parallel Corpora for Ethiopian Languages.

Our transfer-learning-based training process consists of four phases. First, we train on a dataset that is a random mix of all sets totaling up to 1.45 million sentences. Second, we fine-tune the model on Tigrinya using only the Tigrinya portion of the mix. In a third phase, we fine-tune on the training partition of our in-house data. Finally, 200 samples earlier allocated aside from this corpus are used for testing purposes.

As a baseline, we skip the first multilingual training step and use only Tigrinya data to train on.

We see a slight increase in the accuracy of the model on our in-house test set when we use the transfer learning approach. The results in various automatic evaluation metrics are as follows:

Conclusion

Written by Alp öktem, Computational Linguist for Translators without Borders

TWB’s first Arabic translation contest 

We recently held our very first translation contest for Translators without Borders’ thriving community of Arabic translators. Ninety-two talented translators submitted a total of 124 translations on a mixture of humanitarian and literary topics. Each translation was evaluated by fellow community members for accuracy, terminology, and style in order to provide constructive feedback and create greater engagement among the Arabic community.

The winners: humanitarian translation

Shaimaa Elhosan is an English to Arabic translator specializing in humanitarian translation because of her desire to help others. She studied UN translation at the American University in Cairo, which helped her follow her passion.

“I want to help other people, especially children, victims of conflicts, and abused women, people affected by natural disasters. Thus, I volunteered with Translators without Borders (TWB).”

A career highlight as a freelance translator was working on a book titled The Happy Healthy Nonprofit, by Beth Kanter and Aliza Sherman. At that point, Shaimaa realized what a leading role nonprofits play in improving difficult situations.

Shaimaa Elhosan, Arabic translator for Translators without Borders
Shaimaa Elhosan, Arabic translator for TWB

The first story she encountered with TWB was poignant: it told of the daily suffering of victims of war. And it highlighted the misleading images of a comfortable life in camps which too often circulate on social media.

She went on to translate the toolkit for the Global Camp Coordination and Camp Management (CCCM) Cluster, a TWB partner. The toolkit aimed to improve the quality of life and dignity for displaced people living in communal settings. 

Since 2017, Shaimaa has used her skills to translate 8,951 words for TWB and has acquired more valuable experience in humanitarian translation in the process. She continues to study in a constant effort to improve and expand her knowledge.

“I study because I care so much about whether or not the translation communicates the meaning clearly to readers. The TWB team appreciates my know-how and they ask me to participate in more projects. They try hard to support us in translation by providing references and glossaries as much as possible.”

Shaimaa explained that the challenge of communication was made more exciting by this competition. “How could I render the meaning to the readership clearly with such a tricky text? The experience spurred me on to participate in more projects. I’d like to continue to support displaced people, children, and abused women with my work.”

Literary translation

Nabil Salibi won the literary translation category, having received the highest score from his fellow community members. 

Graduation photo from Nabil's Masters in Interpreting and Translating, 2018
Graduation photo from Nabil’s Masters in Interpreting and Translating, 2018

This professional translator is as dedicated to his volunteer projects as he is to his paid work. By volunteering, he hopes to bridge the communication gap between humanitarian organizations and those who seek their support. For Nabil, this means dedicating four to five hours at a time to translate or proof-read texts from his home in Australia.

Since joining TWB in 2016, Nabil has translated 13,592 words. He has focused on projects related to refugees and the conditions in refugee camps, as well as news articles. Nabil also helped translate IFRC’s Global Response Tools Review. That review analyzed the tools we use to respond to disasters, and the risks and challenges related to humanitarian response.

He takes each of these projects seriously:

“Volunteering allows me to appreciate the difficulties imposed by language barriers and the impact on the wellbeing of people who live in communities where they don’t understand the local language.” 

Numerous other translators earned honorable mentions for their efforts. Learn more about their work, and the translation process on the Kató Community Forum.

A shared reward: the language equality initiative

Our highly skilled translators, including Nabil and Shaimaa, will have the opportunity to contribute to Gamayun, the language equality initiative. The goal is to shift control of communication, to allow everyone to share their voice and access information in a language and format they can understand. Using advanced language technology, we’re working with marginalized communities and language specialists to increase language equality and improve two-way communication. Over half of the world’s population simply doesn’t have access to knowledge and information in their own language. Our translators and supporters address this language gap which can prevent people from lifting themselves out of poverty, getting health care, recovering from a crisis, or understanding their rights. Our translators’ efforts enable people to proactively share their needs, concerns, and ideas.

To learn more, click here

What’s next? 

We recently announced two new translation contests open to our French and Swahili translator communities.  If you are already a TWB translator please check the Kató Community Forum for more information. Otherwise, why not join TWB today so you can take part? Entries close on 5 August 2019.

In case you’re looking to take part in a contest, or improve your own translations, our first translation contest winners share some words of advice:  

  • “Make sure you understand the whole article. Context is key.” – Nabil  
  • “I love to translate on paper first.” – Nabil
  • “Never stop reading.” – Shaimaa

 

Written by Danielle Moore, Communications Officer for Translators without Borders.

Interview responses by Shaimaa Elhosan and Nabil Salibi, Translators for 
Translators without Borders.

When words fail: audio recording for verification in multilingual surveys

A survey being conducted in Monguno, Nigeria. Mobile phones and tablets are ubiquitous in humanitarian data collection efforts. Yet most mobile tools do not support continuous audio recording while the survey is being administered. Photo by: Eric DeLuca, Translators without Borders
A survey being conducted in Monguno, Nigeria. Mobile phones and tablets are ubiquitous in humanitarian data collection efforts. Yet most mobile tools do not support continuous audio recording while the survey is being administered. Photo by: Eric DeLuca, Translators without Borders

“Sir, I want to ask you some questions if you agree?”

With that one sentence, our enumerator summarized the 120-word script provided to secure the informed consent of our survey participants – a script designed, in particular, to emphasize that participation would not result in any direct assistance. Humanitarian organizations, research institutes and think tanks around the world are conducting thousands of surveys every year. How many suffer from similar ethical challenges? And how many substandard survey results fall under the radar due to lack of effective quality assurance?

We were conducting a survey on the relationship between internal displacement, cross-border movement, and durable solutions in Borno, a linguistically diverse state in northeast Nigeria. Before data collection began, Translators without Borders (TWB) translated the survey into Hausa and Kanuri to limit the risk of mistranslations due to poor understanding of terminology. Even with this effort, however, not all the enumerators could read Hausa or Kanuri. Although enumerators spent a full day in training going through the translations as a group, there is still a risk that language barriers may have undermined the quality of the research. Humanitarian terminology is often complex, nuanced, and difficult to translate precisely into other languages. A previous study by Translators without Borders in northeastern Nigeria, for example, found that only 57% of enumerators understood the word ‘insurgency’.

We only know the exact phrasing of this interview because we decided to record some of our surveys using an audio recorder. In total, 96 survey interviews were recorded. Fifteen percent of these files were later transcribed into Hausa or Kanuri and translated into English by TWB. Those English transcripts were compared to the enumerator-coded responses, allowing us to analyze the accuracy of our results. While the process was helpful, the findings raise some important concerns.

A digital voice recorder in Maiduguri, Nigeria serves as a simple and low-tech tool for capturing entire surveys. Photo by: Eric DeLuca / Translators without Borders
A digital voice recorder in Maiduguri, Nigeria serves as a simple and low-tech tool for capturing entire surveys. Photo by: Eric DeLuca / Translators without Borders

Consent was not always fully informed

Efforts to obtain informed consent were limited, despite the script provided. According to the consultant, enumerators felt rushed due to the large numbers of people waiting to participate in the survey – but people were interested in participating precisely due to the misbelief that participation could result in assistance, which underlines the need for informed consent. 

Alongside these ethical challenges, the failure to inform participants about the objectives of the research increases the risk of bias in the findings, prompting people to tailor responses to increase their chances of receiving assistance. Problems related to capacity, language, or questionnaire design can also negatively impact survey results, undermining the validity of the findings. 

The enumerator-coded answers did not always match the transcripts

During data quality assurance, we also identified important discrepancies between the interview transcripts and the survey data. In some cases, enumerators had guessed the most likely response rather than properly asking the question, jumping to conclusions based on their understanding of the context rather than respondents’ lived experiences. If the response was unclear, random response options were selected without seeking clarification. Some questions were skipped entirely, but responses still entered into the surveys. The following example, comparing an extract of an interview transcript with the recorded survey data, illustrates these discrepancies. 

Interview transcript Survey data
Interviewer: Do you want to go back to Khaddamari?

Respondent: Yes, I want to.

Interviewer: When do you want to go back?

Respondent: At any time when the peace reigns. You know we are displaced here.

Interviewer: If the place become peaceful, will you go back?

Respondent: If it becomes peaceful, I will go back. 

Do you want to return to Khaddamari in the future? Yes

When do you think you are likely to return? Within the next month

What is the main reason that motivates you to return? Improved safety

What is the second most important reason? Missing home

What is the main issue which currently prevents return to Khaddamari? Food insecurity

What is the second most important issue preventing return? Financial cost of return

At no point in the interview did the respondent mention that he or she was likely to return in the next month. Food insecurity or financial costs were also not cited as factors preventing return. Without audio recordings, we would never have become aware of these issues. Transcribing even just a sample of our audio recordings drew attention to significant problems with the data. Instead of blindly relying on poor quality data, we were able to triangulate information from other sources, and use the interview transcripts as qualitative data. We also included a strongly worded limitations section in the report, acknowledging the data quality issues.

We suspect such data quality issues are common. Surveys, quite simply, are perhaps not the most appropriate tool for data collection in the contexts within which we operate. Certainly, there is a need to be more aware of, and more transparent about, survey limitations.

Despite these limitations, there is no doubt that surveys will continue to be widely used in the humanitarian community and beyond. Surveys are ingrained in the structure and processes of the humanitarian industry. Despite the challenges we faced in Nigeria, we will continue to use surveys ourselves. We know now, however, that audio recordings are invaluable for quality assurance purposes. 

A manual audio recording strategy is difficult to replicate at scale

In an ideal world, all survey interviews would be recorded, transcribed, and translated. This would not only enhance quality assurance processes, but also complement survey data with rich qualitative narratives and quotes. Translating and transcribing recordings, however, requires a huge amount of technical and human resources. 

From a technical standpoint, recording audio files of surveys is not straightforward. Common cell phone data collection tools, such as Kobo, do not offer full-length audio recordings as standard features within surveys. There are also storage issues, as audio files take up significant space on cell phones and stretch the limits of offline survey tools or browser caching. Audio recorders are easy to find and fairly reliable, but they require setting up a parallel workflow and a careful process of coding to ensure that each audio file is appropriately connected to the corresponding survey.

From a time standpoint, this process is slow and involved. As a general rule, it takes roughly six hours to transcribe one hour of audio content. In Hausa and Kanuri – two low resource languages that lack experienced translators – one hour of transcription often took closer to eight hours to complete. The Hausa or Kanuri transcripts then had to be translated into English, a process that took an additional 8 hours. Therefore, each 30-minute recorded survey required about one day of additional work in order to fully process. To put that into perspective, one person would have to work full time every day for close to a year to transcribe and translate a survey involving 350 people.

Language technology can offer some support

In languages such as English or French, solutions already exist to drastically speed up this process. Speech to text technologies – the same technologies used to send SMS messages by voice – have improved dramatically in recent years with the adoption of machine learning approaches. This makes it possible to transcribe and translate audio recordings in a matter of seconds, not days. The error rates of these automated tools are low, and in some cases are even close to rivaling human output. For humanitarians working in contexts with well resourced languages like Spanish, French, or even some dialects of Arabic, these language technologies are already able to offer significant support that makes an audio survey workflow more feasible.

For low-resource languages such as Hausa, Kanuri, Swahili, or Rohingya, these technologies do not exist or are too unreliable. That is because these languages lack the commercial viability to be priority languages for technology companies, and there is often insufficient data to train the machine translation technologies. In an attempt to close the digital language divide, Translators without Borders has recently rolled out an ambitious effort called Gamayun: the language equality initiative. This initiative is working to develop datasets and language technology in low-resource languages relevant to humanitarian and development contexts. The goal is to develop fit-for-purpose solutions that can help break down language barriers and make language solutions such as this more accessible and feasible. Still, this is a long term vision and many of the tools will take months or even years to develop fully.

In the meantime, there are four things you can do now to incorporate audio workflows into your data collection efforts

  1. Record your surveys using tape recorders. It is a valuable process, even if you are limited in how you are able to use the recordings right now. In our experience, enumerators are less likely to intentionally skip entire questions or sections if they know they are being recorded. Work is underway to integrate audio workflows directly into Kobo and other surveying tools, but for now, a tape recorder is an accessible and affordable tool.

  2. Transcribe and translate a small sample of your recordings. Even a handful of transcripts can prove to be useful verification and training tools. We recommend you complete the translations in the pilot stage of your survey, to give you time to adjust trainings or survey design if necessary. This can help to at least provide spot checks of enumerators that you are concerned about, or simply verify one key question, such as the question about informed consent.
  3. Run your recordings through automated transcription and translation tools. This will only be possible if you are working in major languages such as Spanish or French. Technology is rapidly developing, and every month more languages become available and the quality of these technologies improve. Commercially available services are available through Microsoft, Google, and Amazon amongst others, but these services often have a cost, especially at scale.
  4. Partner with TWB to improve technology for low-resource languages. TWB is actively looking for partners to pilot audio recording and transcription processes, to help gather voice and text data to build language technologies for low resource languages. TWB is also seeking partners interested in actively integrating these automated or semi-automated solutions into existing workflows. Get in touch if you are interested in partnering: [email protected]
Written by:

Chloe Sydney, Research Associate at IDMC

Eric DeLuca, Monitoring, Evaluation, and Learning Manager at Translators without Borders

Farewell thoughts from TWB Ambassador Sue Fortescue

In the beginning

Sue Fortescue TWB Ambassador
Credit: ITI/http://markharvey.photoshelter.com/

Like many interesting events in life, my first encounter with Translators without Borders (TWB) was pure serendipity. I was completing the Master of Arts in Audiovisual Translation Studies at the University of Leeds. An excellent component of the course is the series of presentations given by speakers from language service providers, the EU, the UN, and NGOs. In January 2015 Andrew Bredenkamp, Chair of TWB, gave a presentation – and I was hooked!

I had come to translation quite late in life, having worked as a teacher of English as a foreign language in Italy, Nepal, and the UK, then as an IT Manager in Belgium and the US. When I retired I missed the international atmosphere in which I had lived and worked. A friend’s daughter had followed the Leeds Master of Arts course (serendipity again), which is why I enrolled. But I didn’t want to work full time, so volunteering for TWB was the perfect solution.

What was my role?

I started in January 2015, as TWB’s Volunteer Manager, recruiting volunteers and interns to help with our website, accounting, graphics design, and more.  Since then I have hosted stands for TWB at the Institute of Translation and Interpreting (ITI) conferences in Newcastle, Cardiff, and Sheffield. I wrote numerous articles for ITI and CIOL magazines, and gave presentations at various events throughout the UK. I also helped to set up the TWB customer relationship management tool and the criteria for admitting nongovernmental organizations. I even set up (with help!) the TWB Cookbook. And I enjoyed every minute!

TWB was the cover story in the ITI Bulletin September-October 2018, edited by Radhika Holmström

What were the high spots?

Definitely top of the list was meeting, in person or digitally, the many supporters who do so much to make TWB such a successful organization. There are too many to name but I want to single out Noura Tawil, who has lived in Latakia, Syria throughout the war. She is bringing up her children and overcoming hurdles such as intermittent electricity. Throughout all of this, she not only continues to translate texts from English to Arabic for TWB but also supplied several recipes for the TWB Cookbook. Thank you, Noura!

Also high on the list is the satisfaction gained from knowing that I have done something practical, however small, to help people in distress. The first earthquakes in Nepal hit during the ITI Conference in Newcastle in 2015. Having worked in Nepal for three years in the 1970s, I could imagine the distress caused, and was grateful to participants at the conference for spreading the word that we needed translators to and from Newari and Nepali. 

It was also good to know that we helped the refugees fleeing war zones and arriving at the camps in Greece. We did practical things such as translating into Arabic and other languages the instructions on how to register and what to do next. 

It was also very satisfying to know that we helped out after the fire at Grenfell Tower in London, when the British Red Cross asked us to translate the leaflets they were distributing. Our wonderful volunteer translators completed the translations from English into Arabic, Farsi, Pushto, Somali, and Tigrinya, mostly within 24 hours. That made a huge difference to the survivors. 

I gained immense satisfaction from our work during the Ebola crisis, in Nigeria, and our work with the Rohingya escaping to Bangladesh from Myanmar. I’m also grateful to have been able to witness the huge technological advances we have made in our translation tools over the past few years.

It is also gratifying that my work for TWB has been recognized. In 2016 I was presented with the TWB Access to Knowledge Excellence Award. And in 2018 the ITI presented me with its Industry Ambassador Award.

ITI President Sarah Bawa-Mason presents me with the 2018 ITI Industry Ambassador Award. Photo credit: ITI

What next?

I always promised myself that I would step down when I was 70, and I will be 72 this year, so it is time to leave! I will continue to do translations for TWB but will no longer represent TWB through events or writing.

I had always thought that in my retirement I would sit in my rocking chair and read books – but retirement in the 21st century is just not like that! So I will continue with my work as a freelance translator and also my work for organizations such as our local branch of Samaritans

I plan to spend time, especially each summer, on another retirement project – sailing! I volunteered for the 2012 London Olympics as an interpreter (Italian-English and Frech-English) and was assigned to the Paralympic sailing at Portland. I liked what I saw and have since joined my local sailing club, have obtained a Competent Crew certificate and passed the VHF radio exam in order to coordinate communications during races!

I will follow TWB on social media with great interest – and I know that when a crisis strikes anywhere in the world TWB will be there to help. #LanguageMatters

Left to right: Sailing off the Isle of Wight; members of my sailing club holding a fundraising event (I am holding the cash box!); and my RYA ‘Competent Crew’ certificate
Written by Sue Fortescue, Ambassador for Translators without Borders 2015-2019