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        <title>Commonsense Justification for Action Explanation</title>
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    <name type="personal">
        <namePart type="given">Shaohua</namePart>
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    <name type="personal">
        <namePart type="given">Qiaozi</namePart>
        <namePart type="family">Gao</namePart>
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    <name type="personal">
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        <namePart type="family">Sadiya</namePart>
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    <name type="personal">
        <namePart type="given">Joyce</namePart>
        <namePart type="family">Chai</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder(CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results have shown that, compared to a typical attention-based model, CVAE achieves significantly higher performance in both action prediction and justification. A human subject study further shows that the commonsense evidence gathered by CVAE can be communicated to humans to achieve a significantly higher common ground between humans and agents.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1283</identifier>
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        <url>https://aclanthology.org/D18-1283</url>
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    <part>
        <date>2018-oct-nov</date>
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            <start>2627</start>
            <end>2637</end>
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%0 Conference Proceedings
%T Commonsense Justification for Action Explanation
%A Yang, Shaohua
%A Gao, Qiaozi
%A Sadiya, Sari
%A Chai, Joyce
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yang-etal-2018-commonsense
%X To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder(CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results have shown that, compared to a typical attention-based model, CVAE achieves significantly higher performance in both action prediction and justification. A human subject study further shows that the commonsense evidence gathered by CVAE can be communicated to humans to achieve a significantly higher common ground between humans and agents.
%R 10.18653/v1/D18-1283
%U https://aclanthology.org/D18-1283
%U https://doi.org/10.18653/v1/D18-1283
%P 2627-2637

Markdown (Informal)

[Commonsense Justification for Action Explanation](https://aclanthology.org/D18-1283) (Yang et al., EMNLP 2018)

• Commonsense Justification for Action Explanation (Yang et al., EMNLP 2018)

ACL

• Shaohua Yang, Qiaozi Gao, Sari Sadiya, and Joyce Chai. 2018. Commonsense Justification for Action Explanation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2627–2637, Brussels, Belgium. Association for Computational Linguistics.