Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval

Wei Zhong, Jheng-Hong Yang, Yuqing Xie, Jimmy Lin


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.78.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.78.mp4