@inproceedings{xing-etal-2020-improving,
title = "Improving Context Modeling in Neural Topic Segmentation",
author = "Xing, Linzi and
Hackinen, Brad and
Carenini, Giuseppe and
Trebbi, Francesco",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.63/",
doi = "10.18653/v1/2020.aacl-main.63",
pages = "626--636",
abstract = "Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios."
}
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<abstract>Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios.</abstract>
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%0 Conference Proceedings
%T Improving Context Modeling in Neural Topic Segmentation
%A Xing, Linzi
%A Hackinen, Brad
%A Carenini, Giuseppe
%A Trebbi, Francesco
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F xing-etal-2020-improving
%X Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios.
%R 10.18653/v1/2020.aacl-main.63
%U https://aclanthology.org/2020.aacl-main.63/
%U https://doi.org/10.18653/v1/2020.aacl-main.63
%P 626-636
Markdown (Informal)
[Improving Context Modeling in Neural Topic Segmentation](https://aclanthology.org/2020.aacl-main.63/) (Xing et al., AACL 2020)
ACL
- Linzi Xing, Brad Hackinen, Giuseppe Carenini, and Francesco Trebbi. 2020. Improving Context Modeling in Neural Topic Segmentation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 626–636, Suzhou, China. Association for Computational Linguistics.