Papers by Dhruvesh Patel
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization (2021.naacl-main)
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Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula
| Challenge: | Large-scale, open Natural Language Inference datasets have catalyzed the development of NLI models that exhibit close to human-level performance, but the use of these models for other downstream NLP tasks has met with limited success. |
| Approach: | They use multiple-choice reading comprehension and checking factual correctness of textual summarization tasks to investigate potential reasons for this . authors leverage abundance of data from reading comprehension datasets into longer-premise NLI datasets to improve their models . |
| Outcome: | The proposed models outperform models trained on converted datasets due to the difference in premise lengths. |
Reading Comprehension as Natural Language Inference:A Semantic Analysis (2020.starsem-1)
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Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
| Challenge: | In recent past, Natural language Inference (NLI) has gained significant attention, but its true impact has not been well studied. |
| Approach: | They propose to transform a large RACE dataset into an NLI model and compare it to a state-of-the-art model. |
| Outcome: | The proposed model outperforms the previous model on a question-answer concatenation form and a coherent entailment form. |
xLM: A Python Package for Non-Autoregressive Language Models (2026.eacl-demo)
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| Challenge: | Autoregressive language models generate text sequentially from left to right by adding one token at a time. |
| Approach: | They propose a python package that provides a suite of small non-autoregressive language models that can be used by researchers. |
| Outcome: | The proposed package makes implementing small non-autoregressive language models faster and provides a suite of pre-trained models that can be used by the research community. |
Event-Event Relation Extraction using Probabilistic Box Embedding (2022.acl-short)
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| Challenge: | Existing frameworks of event relation extraction do not guarantee coherence between different relation types, such as anti-symmetry. |
| Approach: | They propose to modify existing ERE framework to guarantee coherence by representing each event as a box representation without applying explicit constraints. |
| Outcome: | The proposed model shows stronger conjunctive constraint satisfaction compared to previous models with constraint injection. |
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. |
Box Embeddings: An open-source library for representation learning using geometric structures (2021.emnlp-demo)
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Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum
| Challenge: | Recent studies have explored alternative vector representations with different inductive biases or capabilities. |
| Approach: | They propose a Python library that extends probabilistic box embeddings to geometric shapes and regions. |
| Outcome: | The proposed library is fully open source and compatible with PyTorch and TensorFlow. |
Language Guided Exploration for RL Agents in Text Environments (2024.findings-naacl)
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| Challenge: | Real-world sequential decision making is characterized by sparse rewards and large decision spaces. |
| Approach: | They introduce a language-based framework that provides decision-level guidance to an RL agent. |
| Outcome: | The proposed framework outperforms vanilla RL agents on ScienceWorld in 2022. |