Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

Yixin Nie, Songhe Wang, Mohit Bansal


Abstract
Machine Reading at Scale (MRS) is a challenging task in which a system is given an input query and is asked to produce a precise output by “reading” information from a large knowledge base. The task has gained popularity with its natural combination of information retrieval (IR) and machine comprehension (MC). Advancements in representation learning have led to separated progress in both IR and MC; however, very few studies have examined the relationship and combined design of retrieval and comprehension at different levels of granularity, for development of MRS systems. In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task. The system is evaluated on both fact verification and open-domain multihop QA, achieving state-of-the-art results on the leaderboard test sets of both FEVER and HOTPOTQA. To further demonstrate the importance of semantic retrieval, we present ablation and analysis studies to quantify the contribution of neural retrieval modules at both paragraph-level and sentence-level, and illustrate that intermediate semantic retrieval modules are vital for not only effectively filtering upstream information and thus saving downstream computation, but also for shaping upstream data distribution and providing better data for downstream modeling.
Anthology ID:
D19-1258
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
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2553–2566
Language:
URL:
https://aclanthology.org/D19-1258
DOI:
10.18653/v1/D19-1258
Bibkey:
Cite (ACL):
Yixin Nie, Songhe Wang, and Mohit Bansal. 2019. Revealing the Importance of Semantic Retrieval for Machine Reading at Scale. 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 2553–2566, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (Nie et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1258.pdf
Code
 easonnie/semanticRetrievalMRS +  additional community code
Data
FEVERHotpotQA