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        <title>Joint Slot Filling and Intent Detection via Capsule Neural Networks</title>
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        <namePart type="given">Chenwei</namePart>
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        <namePart type="given">Yaliang</namePart>
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    <abstract>Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.</abstract>
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%0 Conference Proceedings
%T Joint Slot Filling and Intent Detection via Capsule Neural Networks
%A Zhang, Chenwei
%A Li, Yaliang
%A Du, Nan
%A Fan, Wei
%A Yu, Philip
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-etal-2019-joint
%X Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.
%R 10.18653/v1/P19-1519
%U https://aclanthology.org/P19-1519
%U https://doi.org/10.18653/v1/P19-1519
%P 5259-5267

Markdown (Informal)

[Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://aclanthology.org/P19-1519) (Zhang et al., ACL 2019)

• Joint Slot Filling and Intent Detection via Capsule Neural Networks (Zhang et al., ACL 2019)

ACL

• Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip Yu. 2019. Joint Slot Filling and Intent Detection via Capsule Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5259–5267, Florence, Italy. Association for Computational Linguistics.