Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge

Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg, Jianfeng Gao


Abstract
We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources.The key novelty of our method is the introduction of the intermediary modules into the current retriever-reader pipeline.Unlike previous methods that solely rely on the retriever for gathering all evidence in isolation,our intermediary performs a chain of reasoning over the retrieved set.Specifically, our method links the retrieved evidence with its related global context into graphs and organizes them into a candidate list of evidence chains.Built upon pretrained language models, our system achieves competitive performance on two ODQA datasets, OTT-QA and NQ, against tables and passages from Wikipedia.In particular, our model substantially outperforms the previous state-of-the-art on OTT-QA with an exact match score of 47.3 (45% relative gain).
Anthology ID:
2022.findings-emnlp.392
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:
5360–5374
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.392
DOI:
10.18653/v1/2022.findings-emnlp.392
Bibkey:
Cite (ACL):
Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg, and Jianfeng Gao. 2022. Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5360–5374, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge (Ma et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.392.pdf