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.- Anthology ID:
- 2023.findings-emnlp.913
- Original:
- 2023.findings-emnlp.913v1
- Version 2:
- 2023.findings-emnlp.913v2
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13667–13678
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.913
- DOI:
- 10.18653/v1/2023.findings-emnlp.913
- Cite (ACL):
- Sarawoot Kongyoung, Craig MacDonald, and Iadh Ounis. 2023. Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13667–13678, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting (Kongyoung et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.findings-emnlp.913.pdf