REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation

Jinyuan Fang, Zaiqiao Meng, Craig MacDonald

Abstract
Open domain question answering (ODQA) aims to answer questions with knowledge from an external corpus. Fusion-in-Decoder (FiD) is an effective retrieval-augmented reader model to address this task. Given that FiD independently encodes passages, which overlooks the semantic relationships between passages, some studies use knowledge graphs (KGs) to establish dependencies among passages. However, they only leverage knowledge triples from existing KGs, which suffer from incompleteness and may lack certain information critical for answering given questions. To this end, in order to capture the dependencies between passages while tacking the issue of incompleteness in existing KGs, we propose to enhance the retrieval-augmented reader model with a knowledge graph generation module (REANO). Specifically, REANO consists of a KG generator and an answer predictor. The KG generator aims to generate KGs from the passages and the answer predictor then generates answers based on the passages and the generated KGs. Experimental results on five ODQA datasets indicate that compared with baselines, REANO can improve the exact match score by up to 2.7% on the EntityQuestion dataset, with an average improvement of 1.8% across all the datasets.
Anthology ID:
2024.acl-long.115
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2094–2112
Language:
URL:
https://aclanthology.org/2024.acl-long.115
DOI:
Bibkey:
Cite (ACL):
Jinyuan Fang, Zaiqiao Meng, and Craig MacDonald. 2024. REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2094–2112, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation (Fang et al., ACL 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.115.pdf
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