1 code implementation • ACL 2022 • EunJeong Hwang, Jay-Yoon Lee, Tianyi Yang, Dhruvesh Patel, Dongxu Zhang, Andrew McCallum
To understand a story with multiple events, it is important to capture the proper relations across these events.
1 code implementation • 17 Apr 2023 • Dhruvesh Patel, Hamid Eghbalzadeh, Nitin Kamra, Michael Louis Iuzzolino, Unnat Jain, Ruta Desai
Given a succinct natural language goal, e. g., "make a shelf", and a video of the user's progress so far, the aim of VPA is to devise a plan, i. e., a sequence of actions such as "sand shelf", "paint shelf", etc.
no code implementations • 29 Sep 2021 • Jay-Yoon Lee, Dhruvesh Patel, Purujit Goyal, Andrew McCallum
The best version of SEAL that uses NCE ranking method achieves close to +2. 85, +2. 23 respective F1 point gain in average over cross-entropy and INFNET on the feature-based datasets, excluding one outlier that has an excessive gain of +50. 0 F1 points.
1 code implementation • EMNLP (ACL) 2021 • Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations.
1 code implementation • ACL 2022 • Shib Sankar Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Lorraine Li, Andrew McCallum
In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective.
no code implementations • NAACL 2021 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.
no code implementations • Joint Conference on Lexical and Computational Semantics 2020 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
no code implementations • EMNLP (ClinicalNLP) 2020 • Dhruvesh Patel, Sandeep Konam, Sai P. Selvaraj
Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors.
no code implementations • 4 Oct 2020 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
no code implementations • 18 Sep 2020 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
1 code implementation • AKBC 2020 • Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Xiang Li, Luke Vilnis, Andrew McCallum
Box Embeddings [Vilnis et al., 2018, Li et al., 2019] represent concepts with hyperrectangles in $n$-dimensional space and are shown to be capable of modeling tree-like structures efficiently by training on a large subset of the transitive closure of the WordNet hypernym graph.