@inproceedings{ma-etal-2022-open-domain,
title = "Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge",
author = "Ma, Kaixin and
Cheng, Hao and
Liu, Xiaodong and
Nyberg, Eric and
Gao, Jianfeng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.392",
doi = "10.18653/v1/2022.findings-emnlp.392",
pages = "5360--5374",
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).",
}
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<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).</abstract>
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%0 Conference Proceedings
%T Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge
%A Ma, Kaixin
%A Cheng, Hao
%A Liu, Xiaodong
%A Nyberg, Eric
%A Gao, Jianfeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ma-etal-2022-open-domain
%X 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).
%R 10.18653/v1/2022.findings-emnlp.392
%U https://aclanthology.org/2022.findings-emnlp.392
%U https://doi.org/10.18653/v1/2022.findings-emnlp.392
%P 5360-5374
Markdown (Informal)
[Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge](https://aclanthology.org/2022.findings-emnlp.392) (Ma et al., Findings 2022)
ACL