@inproceedings{chen-etal-2020-question,
title = "Question Directed Graph Attention Network for Numerical Reasoning over Text",
author = "Chen, Kunlong and
Xu, Weidi and
Cheng, Xingyi and
Xiaochuan, Zou and
Zhang, Yuyu and
Song, Le and
Wang, Taifeng and
Qi, Yuan and
Chu, Wei",
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.549",
doi = "10.18653/v1/2020.emnlp-main.549",
pages = "6759--6768",
abstract = "Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP.",
}
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<abstract>Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP.</abstract>
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%0 Conference Proceedings
%T Question Directed Graph Attention Network for Numerical Reasoning over Text
%A Chen, Kunlong
%A Xu, Weidi
%A Cheng, Xingyi
%A Xiaochuan, Zou
%A Zhang, Yuyu
%A Song, Le
%A Wang, Taifeng
%A Qi, Yuan
%A Chu, Wei
%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 chen-etal-2020-question
%X Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP.
%R 10.18653/v1/2020.emnlp-main.549
%U https://aclanthology.org/2020.emnlp-main.549
%U https://doi.org/10.18653/v1/2020.emnlp-main.549
%P 6759-6768
Markdown (Informal)
[Question Directed Graph Attention Network for Numerical Reasoning over Text](https://aclanthology.org/2020.emnlp-main.549) (Chen et al., EMNLP 2020)
ACL
- Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, and Wei Chu. 2020. Question Directed Graph Attention Network for Numerical Reasoning over Text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6759–6768, Online. Association for Computational Linguistics.