Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting

Kongyoung, S., Macdonald, C. and Ounis, I. (2023) Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting. In: 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), Singapore, 6-10 Dec 2023, pp. 13667-13678. (doi: 10.18653/v1/2023.findings-emnlp.913)

Abstract

In conversational search settings, users ask questions and receive answers as part of a conversation. The ambiguity in the questions is a common challenge, which can be effectively addressed by leveraging contextual information from the conversation history. In this context, determining topic continuity and reformulating questions into well-defined queries are crucial tasks. Previous approaches have typically addressed these tasks either as a classification task in the case of topic continuity or as a text generation task for question reformulation. However, no prior work has combined both tasks to effectively identify ambiguous questions as part of a conversation. In this paper, we propose a Multi-Task Learning (MTL) approach that uses a text generation model for both question rewriting and classification. Our models, based on BART and T5, are trained to rewrite conversational questions and identify follow-up questions simultaneously. We evaluate our approach on multiple test sets and demonstrate that it outperforms single-task learning baselines on the three LIF test sets, with statistically significant improvements ranging from +3.5% to +10.5% in terms of F1 and Micro-F1 scores. We also show that our approach outperforms single-task question rewriting models in passage retrieval on a large OR-QuAC test set.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kongyoung, Sarawoot and Ounis, Professor Iadh and Macdonald, Professor Craig
• Authors: Kongyoung, S., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science
• Copyright Holders: Copyright © 2023 Association for Computational Linguistics
• First Published: First published in Findings of the Association for Computational Linguistics: EMNLP 2023
• Publisher Policy: Reproduced under a Creative Commons licence

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