Abstract
To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries. In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations. To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mapped-query). Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotation-aware attention is applied on top of history queries to highlight the indicator words. Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models.- Anthology ID:
- 2021.acl-short.95
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 754–758
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.95
- DOI:
- 10.18653/v1/2021.acl-short.95
- Cite (ACL):
- Lingzhi Wang, Xingshan Zeng, and Kam-Fai Wong. 2021. Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 754–758, Online. Association for Computational Linguistics.
- Cite (Informal):
- Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations (Wang et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-short.95.pdf
- Code
- Lingzhi-WANG/Quotation-Recommendation