Papers by Anurag Shukla
INMT-Lite: Accelerating Low-Resource Language Data Collection via Offline Interactive Neural Machine Translation (2024.lrec-main)
Copied to clipboard
Harshita Diddee, Anurag Shukla, Tanuja Ganu, Vivek Seshadri, Sandipan Dandapat, Monojit Choudhury, Kalika Bali
| Challenge: | Interactive Neural Machine Translation (INMT) systems can be used to promote data collection in several under-resourced languages, but are often not adapted to the deployment constraints native language speakers operate in. |
| Approach: | They propose to use interactive neural machine translation systems to promote data collection in several under-resourced languages by integrating three different modes of Internet-independent deployment and four assistive interfaces suitable for data-sparse languages. |
| Outcome: | The proposed model improves the data generation experience of community members along multiple axes without compromising on the quality of the generated translations. |
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi (2020.lrec-1)
Copied to clipboard
Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma, Kalika Bali
| Challenge: | 40% of all the languages in the world face the danger of extinction in the near future . when a language dies out, future generations lose a vital part of the culture that is necessary to completely understand it. |
| Approach: | They propose to use 4 technology-driven methods of data collection to collect data on Gondi, a low-resource vulnerable language spoken by 2.3 million tribal people in south and central India. |
| Outcome: | The proposed methods collected 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. |