A Span-based Dynamic Local Attention Model for Sequential Sentence Classification
Xichen Shang, Qianli Ma, Zhenxi Lin, Jiangyue Yan, Zipeng Chen
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.
BibTeX
@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"
}