@inproceedings{aglionby-teufel-2022-faithful,
title = "Faithful Knowledge Graph Explanations in Commonsense Question Answering",
author = "Aglionby, Guy and
Teufel, Simone",
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.743",
doi = "10.18653/v1/2022.emnlp-main.743",
pages = "10811--10817",
abstract = "Knowledge graphs are commonly used as sources of information in commonsense question answering, and can also be used to express explanations for the model{'}s answer choice. A common way of incorporating facts from the graph is to encode them separately from the question, and then combine the two representations to select an answer. In this paper, we argue that highly faithful graph-based explanations cannot be extracted from existing models of this type. Such explanations will not include reasoning done by the transformer encoding the question, so will be incomplete. We confirm this theory with a novel proxy measure for faithfulness and propose two architecture changes to address the problem. Our findings suggest a path forward for developing architectures for faithful graph-based explanations.",
}
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%0 Conference Proceedings
%T Faithful Knowledge Graph Explanations in Commonsense Question Answering
%A Aglionby, Guy
%A Teufel, Simone
%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 aglionby-teufel-2022-faithful
%X Knowledge graphs are commonly used as sources of information in commonsense question answering, and can also be used to express explanations for the model’s answer choice. A common way of incorporating facts from the graph is to encode them separately from the question, and then combine the two representations to select an answer. In this paper, we argue that highly faithful graph-based explanations cannot be extracted from existing models of this type. Such explanations will not include reasoning done by the transformer encoding the question, so will be incomplete. We confirm this theory with a novel proxy measure for faithfulness and propose two architecture changes to address the problem. Our findings suggest a path forward for developing architectures for faithful graph-based explanations.
%R 10.18653/v1/2022.emnlp-main.743
%U https://aclanthology.org/2022.emnlp-main.743
%U https://doi.org/10.18653/v1/2022.emnlp-main.743
%P 10811-10817
Markdown (Informal)
[Faithful Knowledge Graph Explanations in Commonsense Question Answering](https://aclanthology.org/2022.emnlp-main.743) (Aglionby & Teufel, EMNLP 2022)
ACL