How machine translation can help bring Covid-19 info to the masses

After obtaining his PhD from Dublin City University (DCU) in 2011, Dr Rejwanul Haque entered into the language technology industry and worked on industrial machine translation (MT) solutions for seven years.

He re-joined DCU’s MT team in 2018 and worked as an industry-oriented postdoc at SFI’s Adapt research centre. Since January 2019, he has been working as a research fellow with a Marie Skłodowska-Curie Fellowship.

His research is supported by the Euraxess Hosting Agreement Scheme, which enables approved research enterprises to recruit experts from outside the European Economic Area for their R&D departments in Ireland.

What inspired you to become a researcher?

Prior to my PhD, I obtained my degree from Jadavpur University, India, where I worked as a research engineer with the Ministry of Communication and Information Technology. This was part of a sponsored consortia-based project, ‘cross-lingual information access’ (CLIA), for two years.

From that time, I confronted many profound challenges in relation to the project and worked on different natural language processing (NLP) problems such as parts-of-speech tagging, named entity recognition and MT. My interest in this area of research grew out over time during my tenure in the CLIA project.

Can you tell us about the research you’re currently working on?

I primarily work on MT, which is arguably regarded as the most difficult problems scientists could ever contemplate doing on a computer.

These problems include terminology translation, knowledge distillation, interactive MT, low-resource MT, data selection and domain adaptation. Although my primary research area is MT, my interests also include other NLP problems such as question-answering, social media analytics and information extraction.

In your opinion, why is your research important?

Every day more people are becoming infected and dying across the world due to Covid-19 pandemic. In cases where language is a barrier to access of pertinent information, MT may help people assimilate information published in different languages.

As part of the DCU MT team, we have recently built eight multilingual MT engines that are specifically trained to translate Covid-19 material between German, French, Italian, Spanish into English, as well as the reverse direction.

We have enabled online public access to the systems where users can select their source and target languages via a drop-down menu, and paste their desired text into the source panel. The language-appropriate MT server carries out the translation, and the translation is instantaneously retrieved to appear in the source panel.

We have already published this research in ArXiv with the hope of contributing to the fight against Covid-19 and to have a direct impact on society.

This article first appeared on Silicon Republic, full article can be viewed here.