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arXiv · 1904.09491

Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

Guokan Shang·Antoine Jean-Pierre Tixier·Michalis Vazirgiannis·Jean-Pierre Lorré
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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.

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Guokan Shang, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis, Jean-Pierre Lorré. 2019-11-08. Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding. https://arxiv.org/abs/1904.09491

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