Related papers: Reasoning over Hybrid Chain for Table-and-Text Open Domain QA

Reasoning over Hybrid Chain for Table-and-Text Open Domain QA

URL: http://arxiv.org/abs/2201.05880v1
Date: Sat, 15 Jan 2022 16:11:55 GMT
Title: Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
Authors: Wanjun Zhong, Junjie Huang, Qian Liu, Ming Zhou, Jiahai Wang, Jian Yin, and Nan Duan
Abstract summary: We propose a ChAin-centric Reasoning and Pre-training framework (CARP) CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process.
Score: 69.8436986668218
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method constructs the large-scale reasoning corpus by synthesizing pseudo heterogeneous reasoning paths from Wikipedia and generating corresponding questions. We evaluate our system on OTT-QA, a large-scale table-and-text open-domain question answering benchmark, and our system achieves the state-of-the-art performance. Further analyses illustrate that the explicit hybrid chain offers substantial performance improvement and interpretablity of the intermediate reasoning process, and the chain-centric pre-training boosts the performance on the chain extraction.

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