Papers by Vaibhav Sharma
Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus (2025.findings-emnlp)
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| Challenge: | linguistic diversity of India poses significant machine translation challenges, authors say . underrepresented tribal languages like Bhili lack high-quality linguistic resources . |
| Approach: | They introduce a Bhili-Hindi-English Parallel Corpus, the first and largest parallel corpus worldwide . they evaluated a wide range of proprietary and open-source MLLMs on bidirectional translation tasks . |
| Outcome: | The proposed corpus spans critical domains such as education, administration, and news. |
CheckersGPT: Learning World Models through Language Modeling (2024.acl-srw)
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| Challenge: | Large Language Models (LLMs) have shown impressive performance on various tasks, but the underlying process behind predicting the desired next token remains a black box. |
| Approach: | They train a GPT-style autoregressive language model using only the next character prediction objective and then train corresponding model with different layer sizes. |
| Outcome: | The proposed model shows a hint of learning a world model representation of the board positions on a simulated game of checkers and human gameplay dataset. |
PoseStitch-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation (2025.emnlp-main)
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| Challenge: | Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. |
| Approach: | They propose a pose-based pre-training scheme that is inspired by a linguistic-templates-based sentence generation technique. |
| Outcome: | The proposed pre-training scheme outperforms state-of-the-art methods for pose-based gloss-free translation on two sign language datasets. |