Papers by Siddhartha Mishra
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings (2022.acl-long)
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Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum
| Challenge: | Word2Box provides a set-theoretic training objective for learning word representations . word representation is not natural, all senses and contexts, levels of abstraction, variants and modifications which the word may represent are forced to be captured by mat t is nunc. |
| Approach: | They propose a fuzzy-set interpretation of box embeddings and learn box representations of words using a set-theoretic training objective. |
| Outcome: | The proposed model improves word similarity tasks on less common words. |
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |