Abstract
This work extends the current quotation recommendation task to a more realistic quotation recommender system that learns to predict when to quote and what to quote jointly. The system consists of three modules (tasks), a prediction module to predict whether to quote given conversation contexts, a recommendation module to recommend suitable quotations and a generation module generating quotations or sentences in ordinary language to continue the conversation. We benchmark several competitive models for the two newly introduced tasks (i.e., when-to-quote and what-to-continue). For quotation recommendation, compared with previous work that is either generation-based or ranking-based recommendation, we propose a novel framework with mutual promotion of generation module and ranking-based recommendation module. Experiments show that our framework achieves significantly better performance than baselines on two datasets. Further experiments and analyses validate the effectiveness of the proposed mechanisms and get a better understanding of the quotation recommendation task.- Anthology ID:
- 2022.findings-emnlp.225
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3094–3105
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.225
- DOI:
- 10.18653/v1/2022.findings-emnlp.225
- Cite (ACL):
- Lingzhi Wang, Xingshan Zeng, and Kam-Fai Wong. 2022. Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3094–3105, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation (Wang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.findings-emnlp.225.pdf