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        <title>Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training</title>
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    <name type="personal">
        <namePart type="given">Zhongyang</namePart>
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    <name type="personal">
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        <namePart type="family">Ding</namePart>
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        <namePart type="given">Ting</namePart>
        <namePart type="family">Liu</namePart>
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        <dateIssued>2018-08</dateIssued>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE). However, the pure MLE training objective much limits the power of Seq2Seq model in generating high-quality storys. In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context. Our model includes a generator that defines the policy of generating a story ending, and a discriminator that labels story endings as human-generated or machine-generated. Carefully designed human and automatic evaluation metrics demonstrate that our adversarial training augmented Seq2Seq model can generate more reasonable and diversified story endings compared to purely MLE-trained Seq2Seq model. Moreover, our model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6% compared with state-of-the-art baseline methods.</abstract>
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        <url>https://aclanthology.org/C18-1088</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training
%A Li, Zhongyang
%A Ding, Xiao
%A Liu, Ting
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F li-etal-2018-generating
%X Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE). However, the pure MLE training objective much limits the power of Seq2Seq model in generating high-quality storys. In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context. Our model includes a generator that defines the policy of generating a story ending, and a discriminator that labels story endings as human-generated or machine-generated. Carefully designed human and automatic evaluation metrics demonstrate that our adversarial training augmented Seq2Seq model can generate more reasonable and diversified story endings compared to purely MLE-trained Seq2Seq model. Moreover, our model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6% compared with state-of-the-art baseline methods.
%U https://aclanthology.org/C18-1088
%P 1033-1043

Markdown (Informal)

[Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training](https://aclanthology.org/C18-1088) (Li et al., COLING 2018)

• Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training (Li et al., COLING 2018)

ACL

• Zhongyang Li, Xiao Ding, and Ting Liu. 2018. Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1033–1043, Santa Fe, New Mexico, USA. Association for Computational Linguistics.