@inproceedings{shang-etal-2020-energy,
title = "Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding",
author = "Shang, Guokan and
Tixier, Antoine and
Vazirgiannis, Michalis and
Lorr{\'e}, Jean-Pierre",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
booktitle = "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",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.34/",
doi = "10.18653/v1/2020.aacl-main.34",
pages = "313--327",
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."
}
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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.
%R 10.18653/v1/2020.aacl-main.34
%U https://aclanthology.org/2020.aacl-main.34/
%U https://doi.org/10.18653/v1/2020.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)
ACL