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        <title>Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding</title>
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        <namePart type="given">Guokan</namePart>
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    <name type="personal">
        <namePart type="given">Michalis</namePart>
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    <name type="personal">
        <namePart type="given">Jean-Pierre</namePart>
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            <namePart type="given">Kam-Fai</namePart>
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    <abstract>Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.</abstract>
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%0 Conference Proceedings
%T Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
%A Shang, Guokan
%A Tixier, Antoine
%A Vazirgiannis, Michalis
%A Lorré, Jean-Pierre
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F shang-etal-2020-energy
%X Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.
%U https://aclanthology.org/2020.aacl-main.34
%P 313-327

Markdown (Informal)

[Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding](https://aclanthology.org/2020.aacl-main.34) (Shang et al., AACL 2020)

• Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding (Shang et al., AACL 2020)

ACL

• Guokan Shang, Antoine Tixier, Michalis Vazirgiannis, and Jean-Pierre Lorré. 2020. Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 313–327, Suzhou, China. Association for Computational Linguistics.