@inproceedings{wang-etal-2018-toward,
title = "Toward Fast and Accurate Neural Discourse Segmentation",
author = "Wang, Yizhong and
Li, Sujian and
Yang, Jingfeng",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1116",
doi = "10.18653/v1/D18-1116",
pages = "962--967",
abstract = "Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.",
}
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<abstract>Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Toward Fast and Accurate Neural Discourse Segmentation
%A Wang, Yizhong
%A Li, Sujian
%A Yang, Jingfeng
%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 wang-etal-2018-toward
%X Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.
%R 10.18653/v1/D18-1116
%U https://aclanthology.org/D18-1116
%U https://doi.org/10.18653/v1/D18-1116
%P 962-967
Markdown (Informal)
[Toward Fast and Accurate Neural Discourse Segmentation](https://aclanthology.org/D18-1116) (Wang et al., EMNLP 2018)
ACL
- Yizhong Wang, Sujian Li, and Jingfeng Yang. 2018. Toward Fast and Accurate Neural Discourse Segmentation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 962–967, Brussels, Belgium. Association for Computational Linguistics.