@inproceedings{song-etal-2020-discourse,
title = "Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays",
author = "Song, Wei and
Song, Ziyao and
Fu, Ruiji and
Liu, Lizhen and
Cheng, Miaomiao and
Liu, Ting",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.225",
doi = "10.18653/v1/2020.emnlp-main.225",
pages = "2820--2830",
abstract = "This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays. Specifically, we focus on two issues. First, we propose structural sentence positional encodings to explicitly represent sentence positions. Second, we propose to use inter-sentence attentions to capture sentence interactions and enhance sentence representation. We conduct experiments on two datasets: a Chinese dataset and an English dataset. We find that (i) sentence positional encoding can lead to a large improvement for identifying discourse elements; (ii) a structural relative positional encoding of sentences shows to be most effective; (iii) inter-sentence attention vectors are useful as a kind of sentence representations for identifying discourse elements.",
}
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<abstract>This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays. Specifically, we focus on two issues. First, we propose structural sentence positional encodings to explicitly represent sentence positions. Second, we propose to use inter-sentence attentions to capture sentence interactions and enhance sentence representation. We conduct experiments on two datasets: a Chinese dataset and an English dataset. We find that (i) sentence positional encoding can lead to a large improvement for identifying discourse elements; (ii) a structural relative positional encoding of sentences shows to be most effective; (iii) inter-sentence attention vectors are useful as a kind of sentence representations for identifying discourse elements.</abstract>
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%0 Conference Proceedings
%T Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays
%A Song, Wei
%A Song, Ziyao
%A Fu, Ruiji
%A Liu, Lizhen
%A Cheng, Miaomiao
%A Liu, Ting
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F song-etal-2020-discourse
%X This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays. Specifically, we focus on two issues. First, we propose structural sentence positional encodings to explicitly represent sentence positions. Second, we propose to use inter-sentence attentions to capture sentence interactions and enhance sentence representation. We conduct experiments on two datasets: a Chinese dataset and an English dataset. We find that (i) sentence positional encoding can lead to a large improvement for identifying discourse elements; (ii) a structural relative positional encoding of sentences shows to be most effective; (iii) inter-sentence attention vectors are useful as a kind of sentence representations for identifying discourse elements.
%R 10.18653/v1/2020.emnlp-main.225
%U https://aclanthology.org/2020.emnlp-main.225
%U https://doi.org/10.18653/v1/2020.emnlp-main.225
%P 2820-2830
Markdown (Informal)
[Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays](https://aclanthology.org/2020.emnlp-main.225) (Song et al., EMNLP 2020)
ACL