@inproceedings{zhang-etal-2020-table,
title = "Table Fact Verification with Structure-Aware Transformer",
author = "Zhang, Hongzhi and
Wang, Yingyao and
Wang, Sirui and
Cao, Xuezhi and
Zhang, Fuzheng and
Wang, Zhongyuan",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.126",
doi = "10.18653/v1/2020.emnlp-main.126",
pages = "1624--1629",
abstract = "Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied to encode tables, because simply linearizing tables into sequences will lose the cell alignment information. To better utilize pre-trained transformers for table representation, we propose a Structure-Aware Transformer (SAT), which injects the table structural information into the mask of the self-attention layer. A method to combine symbolic and linguistic reasoning is also explored for this task. Our method outperforms baseline with 4.93{\%} on TabFact, a large scale table verification dataset.",
}
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<abstract>Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied to encode tables, because simply linearizing tables into sequences will lose the cell alignment information. To better utilize pre-trained transformers for table representation, we propose a Structure-Aware Transformer (SAT), which injects the table structural information into the mask of the self-attention layer. A method to combine symbolic and linguistic reasoning is also explored for this task. Our method outperforms baseline with 4.93% on TabFact, a large scale table verification dataset.</abstract>
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%0 Conference Proceedings
%T Table Fact Verification with Structure-Aware Transformer
%A Zhang, Hongzhi
%A Wang, Yingyao
%A Wang, Sirui
%A Cao, Xuezhi
%A Zhang, Fuzheng
%A Wang, Zhongyuan
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-table
%X Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied to encode tables, because simply linearizing tables into sequences will lose the cell alignment information. To better utilize pre-trained transformers for table representation, we propose a Structure-Aware Transformer (SAT), which injects the table structural information into the mask of the self-attention layer. A method to combine symbolic and linguistic reasoning is also explored for this task. Our method outperforms baseline with 4.93% on TabFact, a large scale table verification dataset.
%R 10.18653/v1/2020.emnlp-main.126
%U https://aclanthology.org/2020.emnlp-main.126
%U https://doi.org/10.18653/v1/2020.emnlp-main.126
%P 1624-1629
Markdown (Informal)
[Table Fact Verification with Structure-Aware Transformer](https://aclanthology.org/2020.emnlp-main.126) (Zhang et al., EMNLP 2020)
ACL
- Hongzhi Zhang, Yingyao Wang, Sirui Wang, Xuezhi Cao, Fuzheng Zhang, and Zhongyuan Wang. 2020. Table Fact Verification with Structure-Aware Transformer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1624–1629, Online. Association for Computational Linguistics.