Papers by Shubham Agarwal
Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)
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Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler
| Challenge: | Named entity recognition is a technique for extracting information from unstructured data with known labels. |
| Approach: | They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases . |
| Outcome: | The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods . |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
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Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |
History for Visual Dialog: Do we really need it? (2020.acl-main)
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| Challenge: | Recent studies have shown that dialog-based interaction grounded in visual information is not as effective as previous VQA tasks because of its dialog history. |
| Approach: | They propose a visual dialogue subset which explicitly encodes dialog history and a NDCG benchmark of 63%. |
| Outcome: | The proposed subset (VisdialConv) of the VisdialVal set achieves state-of-the-art performance on 72 % of the data. |
A Systematic Review of Reproducibility Research in Natural Language Processing (2021.eacl-main)
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| Challenge: | Despite the recent progress in reproducibility, the field is far from reaching a consensus on how reproducibility should be defined, measured and addressed. |
| Approach: | They propose to provide a wide-angle snapshot of current work on reproducibility in NLP. |
| Outcome: | The proposed work will provide a wide-angle snapshot of current work on reproducibility in NLP. |
MUTANT: A Recipe for Multilingual Tokenizer Design (2026.acl-long)
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| Challenge: | Subword tokenization schemes such as Byte Pair Encoding (BPE) are widely adopted, but their effectiveness in multilingual settings remains understudied. |
| Approach: | They propose a multilingual tokenizer that produces linguistically coherent tokens for multilingual LLMs. |
| Outcome: | The proposed tokenizer improves fertility score by 39.5% over LLaMA4 and 18% over Sutra. |
SandhiKosh: A Benchmark Corpus for Evaluating Sanskrit Sandhi Tools (L18-1)
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| Challenge: | Several important texts which are of interest to people all over the world were written in Sanskrit. |
| Approach: | They develop a Sanskrit benchmark to evaluate the completeness and accuracy of tools . they use three most prominent tools to evaluate their completeness . |
| Outcome: | The proposed tools have substantial scope for improvement and are available to researchers worldwide. |