Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval

Zeynep Akkalyoncu Yilmaz, Wei Yang, Haotian Zhang, Jimmy Lin

Abstract
This paper applies BERT to ad hoc document retrieval on news articles, which requires addressing two challenges: relevance judgments in existing test collections are typically provided only at the document level, and documents often exceed the length that BERT was designed to handle. Our solution is to aggregate sentence-level evidence to rank documents. Furthermore, we are able to leverage passage-level relevance judgments fortuitously available in other domains to fine-tune BERT models that are able to capture cross-domain notions of relevance, and can be directly used for ranking news articles. Our simple neural ranking models achieve state-of-the-art effectiveness on three standard test collections.
Anthology ID:
D19-1352
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3490–3496
Language:
URL:
https://aclanthology.org/D19-1352
DOI:
10.18653/v1/D19-1352
Bibkey:
Cite (ACL):
Zeynep Akkalyoncu Yilmaz, Wei Yang, Haotian Zhang, and Jimmy Lin. 2019. Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3490–3496, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval (Akkalyoncu Yilmaz et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/landing_page/D19-1352.pdf
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