Abstract
With the recent success of dense retrieval methods based on bi-encoders, studies have applied this approach to various interesting downstream retrieval tasks with good efficiency and in-domain effectiveness. Recently, we have also seen the presence of dense retrieval models in Math Information Retrieval (MIR) tasks,but the most effective systems remain classic retrieval methods that consider hand-crafted structure features. In this work, we try to combine the best of both worlds: a well-defined structure search method for effective formula search and efficient bi-encoder dense retrieval models to capture contextual similarities. Specifically, we have evaluated two representative bi-encoder models for token-level and passage-level dense retrieval on recent MIR tasks. Our results show that bi-encoder models are highly complementary to existing structure search methods, and we are able to advance the state-of-the-art on MIR datasets.- Anthology ID:
- 2022.findings-emnlp.78
- 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:
- 1092–1102
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.78
- DOI:
- 10.18653/v1/2022.findings-emnlp.78
- Cite (ACL):
- Wei Zhong, Jheng-Hong Yang, Yuqing Xie, and Jimmy Lin. 2022. Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1092–1102, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval (Zhong et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/alta-23-ingestion/2022.findings-emnlp.78.pdf