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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-emnlp.392.pdf