@inproceedings{wu-etal-2018-attention,
title = "Attention-based Semantic Priming for Slot-filling",
author = "Wu, Jiewen and
Banchs, Rafael E. and
D{'}Haro, Luis Fernando and
Krishnaswamy, Pavitra and
Chen, Nancy",
editor = "Chen, Nancy and
Banchs, Rafael E. and
Duan, Xiangyu and
Zhang, Min and
Li, Haizhou",
booktitle = "Proceedings of the Seventh Named Entities Workshop",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2404",
doi = "10.18653/v1/W18-2404",
pages = "22--26",
abstract = "The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attention-based RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pre-trained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9{\%} improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.",
}
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<abstract>The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attention-based RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pre-trained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9% improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.</abstract>
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%0 Conference Proceedings
%T Attention-based Semantic Priming for Slot-filling
%A Wu, Jiewen
%A Banchs, Rafael E.
%A D’Haro, Luis Fernando
%A Krishnaswamy, Pavitra
%A Chen, Nancy
%Y Chen, Nancy
%Y Banchs, Rafael E.
%Y Duan, Xiangyu
%Y Zhang, Min
%Y Li, Haizhou
%S Proceedings of the Seventh Named Entities Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wu-etal-2018-attention
%X The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attention-based RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pre-trained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9% improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.
%R 10.18653/v1/W18-2404
%U https://aclanthology.org/W18-2404
%U https://doi.org/10.18653/v1/W18-2404
%P 22-26
Markdown (Informal)
[Attention-based Semantic Priming for Slot-filling](https://aclanthology.org/W18-2404) (Wu et al., NEWS 2018)
ACL
- Jiewen Wu, Rafael E. Banchs, Luis Fernando D’Haro, Pavitra Krishnaswamy, and Nancy Chen. 2018. Attention-based Semantic Priming for Slot-filling. In Proceedings of the Seventh Named Entities Workshop, pages 22–26, Melbourne, Australia. Association for Computational Linguistics.