Papers by Abhishek Agarwal
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
| Approach: | They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations . |
| Outcome: | The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient . |
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines (2020.lrec-1)
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
| Challenge: | MultiWOZ 2.0 has substantial noise in dialogue state annotations and dialogue utterances . follow-up work has augmented the original dataset with user dialogue acts . |
| Approach: | They propose to reannotate dialogue state and utterances based on original dataset . they then compare their results to other datasets to improve their models . |
| Outcome: | The proposed dataset improves on the noise in the dialogue state annotations and dialogue utterances. |
Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance (2022.findings-emnlp)
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| Challenge: | Summarizing legal decisions requires the expertise of law practitioners, which is time- and cost-intensive. |
| Approach: | They propose methods for extracting summarized legal decisions using limited expert annotated data. |
| Outcome: | The proposed models achieve ROUGE scores vis-à-vis expert extracted summaries that match inter-annotator comparisons. |