@inproceedings{mao-etal-2022-explainable,
title = "Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning",
author = "Mao, Jianguo and
Jiang, Wenbin and
Wang, Xiangdong and
Liu, Hong and
Xia, Yu and
Lyu, Yajuan and
She, QiaoQiao",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.356",
doi = "10.18653/v1/2022.emnlp-main.356",
pages = "5318--5325",
abstract = "Multi-hop Question Answering is an agent task for testing the reasoning ability. With the development of pre-trained models, the implicit reasoning ability has been surprisingly improved and can even surpass human performance. However, the nature of the black box hinders the construction of explainable intelligent systems. Several researchers have explored explainable neural-symbolic reasoning methods based on question decomposition techniques. The undifferentiable symbolic operations and the error propagation in the reasoning process lead to poor performance. To alleviate it, we propose a simple yet effective Global Differentiable Learning strategy to explore optimal reasoning paths from the latent probability space so that the model learns to solve intermediate reasoning processes without expert annotations. We further design a Dynamic Adaptive Reasoner to enhance the generalization of unseen questions. Our method achieves 17{\%} improvements in F1-score against BreakRC and shows better interpretability. We take a step forward in building interpretable reasoning methods.",
}
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%0 Conference Proceedings
%T Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning
%A Mao, Jianguo
%A Jiang, Wenbin
%A Wang, Xiangdong
%A Liu, Hong
%A Xia, Yu
%A Lyu, Yajuan
%A She, QiaoQiao
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mao-etal-2022-explainable
%X Multi-hop Question Answering is an agent task for testing the reasoning ability. With the development of pre-trained models, the implicit reasoning ability has been surprisingly improved and can even surpass human performance. However, the nature of the black box hinders the construction of explainable intelligent systems. Several researchers have explored explainable neural-symbolic reasoning methods based on question decomposition techniques. The undifferentiable symbolic operations and the error propagation in the reasoning process lead to poor performance. To alleviate it, we propose a simple yet effective Global Differentiable Learning strategy to explore optimal reasoning paths from the latent probability space so that the model learns to solve intermediate reasoning processes without expert annotations. We further design a Dynamic Adaptive Reasoner to enhance the generalization of unseen questions. Our method achieves 17% improvements in F1-score against BreakRC and shows better interpretability. We take a step forward in building interpretable reasoning methods.
%R 10.18653/v1/2022.emnlp-main.356
%U https://aclanthology.org/2022.emnlp-main.356
%U https://doi.org/10.18653/v1/2022.emnlp-main.356
%P 5318-5325
Markdown (Informal)
[Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning](https://aclanthology.org/2022.emnlp-main.356) (Mao et al., EMNLP 2022)
ACL