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        <title>A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding</title>
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    <name type="personal">
        <namePart type="given">Changliang</namePart>
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        <namePart type="given">Liang</namePart>
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    <name type="personal">
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        <namePart type="family">Qi</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue systems. Joint learning has shown to be effective in SLU given that slot tags and intents are supposed to share knowledge with each other. However, most existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. In this work, we propose a novel self-attentive model with gate mechanism to fully utilize the semantic correlation between slot and intent. Our model first obtains intent-augmented embeddings based on neural network with self-attention mechanism. And then the intent semantic representation is utilized as the gate for labelling slot tags. The objectives of both tasks are optimized simultaneously via joint learning in an end-to-end way. We conduct experiment on popular benchmark ATIS. The results show that our model achieves state-of-the-art and outperforms other popular methods by a large margin in terms of both intent detection error rate and slot filling F1-score. This paper gives a new perspective for research on SLU.</abstract>
    <identifier type="citekey">li-etal-2018-self</identifier>
    <identifier type="doi">10.18653/v1/D18-1417</identifier>
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        <url>https://aclanthology.org/D18-1417/</url>
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        <date>2018-oct-nov</date>
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            <start>3824</start>
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%0 Conference Proceedings
%T A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding
%A Li, Changliang
%A Li, Liang
%A Qi, Ji
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F li-etal-2018-self
%X Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue systems. Joint learning has shown to be effective in SLU given that slot tags and intents are supposed to share knowledge with each other. However, most existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. In this work, we propose a novel self-attentive model with gate mechanism to fully utilize the semantic correlation between slot and intent. Our model first obtains intent-augmented embeddings based on neural network with self-attention mechanism. And then the intent semantic representation is utilized as the gate for labelling slot tags. The objectives of both tasks are optimized simultaneously via joint learning in an end-to-end way. We conduct experiment on popular benchmark ATIS. The results show that our model achieves state-of-the-art and outperforms other popular methods by a large margin in terms of both intent detection error rate and slot filling F1-score. This paper gives a new perspective for research on SLU.
%R 10.18653/v1/D18-1417
%U https://aclanthology.org/D18-1417/
%U https://doi.org/10.18653/v1/D18-1417
%P 3824-3833

Markdown (Informal)

[A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding](https://aclanthology.org/D18-1417/) (Li et al., EMNLP 2018)

• A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding (Li et al., EMNLP 2018)

ACL

• Changliang Li, Liang Li, and Ji Qi. 2018. A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3824–3833, Brussels, Belgium. Association for Computational Linguistics.