@inproceedings{raheja-tetreault-2019-dialogue,
title = "{D}ialogue {A}ct {C}lassification with {C}ontext-{A}ware {S}elf-{A}ttention",
author = "Raheja, Vipul and
Tetreault, Joel",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1373",
doi = "10.18653/v1/N19-1373",
pages = "3727--3733",
abstract = "Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.",
}
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%0 Conference Proceedings
%T Dialogue Act Classification with Context-Aware Self-Attention
%A Raheja, Vipul
%A Tetreault, Joel
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F raheja-tetreault-2019-dialogue
%X Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.
%R 10.18653/v1/N19-1373
%U https://aclanthology.org/N19-1373
%U https://doi.org/10.18653/v1/N19-1373
%P 3727-3733
Markdown (Informal)
[Dialogue Act Classification with Context-Aware Self-Attention](https://aclanthology.org/N19-1373) (Raheja & Tetreault, NAACL 2019)
ACL
- Vipul Raheja and Joel Tetreault. 2019. Dialogue Act Classification with Context-Aware Self-Attention. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3727–3733, Minneapolis, Minnesota. Association for Computational Linguistics.