@inproceedings{shang-etal-2021-span,
title = "A Span-based Dynamic Local Attention Model for Sequential Sentence Classification",
author = "Shang, Xichen and
Ma, Qianli and
Lin, Zhenxi and
Yan, Jiangyue and
Chen, Zipeng",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.26",
doi = "10.18653/v1/2021.acl-short.26",
pages = "198--203",
abstract = "Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.",
}
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<abstract>Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.</abstract>
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%0 Conference Proceedings
%T A Span-based Dynamic Local Attention Model for Sequential Sentence Classification
%A Shang, Xichen
%A Ma, Qianli
%A Lin, Zhenxi
%A Yan, Jiangyue
%A Chen, Zipeng
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shang-etal-2021-span
%X Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.
%R 10.18653/v1/2021.acl-short.26
%U https://aclanthology.org/2021.acl-short.26
%U https://doi.org/10.18653/v1/2021.acl-short.26
%P 198-203
Markdown (Informal)
[A Span-based Dynamic Local Attention Model for Sequential Sentence Classification](https://aclanthology.org/2021.acl-short.26) (Shang et al., ACL-IJCNLP 2021)
ACL
- Xichen Shang, Qianli Ma, Zhenxi Lin, Jiangyue Yan, and Zipeng Chen. 2021. A Span-based Dynamic Local Attention Model for Sequential Sentence Classification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 198–203, Online. Association for Computational Linguistics.