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    <titleInfo>
        <title>One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment</title>
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    <name type="personal">
        <namePart type="given">Tengfei</namePart>
        <namePart type="family">Huo</namePart>
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        <namePart type="given">Zhiqiang</namePart>
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    <name type="personal">
        <namePart type="given">Jinchao</namePart>
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    <name type="personal">
        <namePart type="given">Jie</namePart>
        <namePart type="family">Zhou</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.259</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment
%A Huo, Tengfei
%A Liu, Zhiqiang
%A Zhang, Jinchao
%A Zhou, Jie
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F huo-etal-2020-one
%X The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin.
%R 10.18653/v1/2020.coling-main.259
%U https://aclanthology.org/2020.coling-main.259
%U https://doi.org/10.18653/v1/2020.coling-main.259
%P 2889-2899

Markdown (Informal)

[One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment](https://aclanthology.org/2020.coling-main.259) (Huo et al., COLING 2020)

• One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment (Huo et al., COLING 2020)

ACL

• Tengfei Huo, Zhiqiang Liu, Jinchao Zhang, and Jie Zhou. 2020. One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2889–2899, Barcelona, Spain (Online). International Committee on Computational Linguistics.