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        <title>Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring</title>
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        <namePart type="given">Farjana</namePart>
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        <namePart type="given">Naoya</namePart>
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            <namePart type="given">Eunsol</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers. In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation. Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.</abstract>
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%0 Conference Proceedings
%T Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring
%A Mim, Farjana Sultana
%A Inoue, Naoya
%A Reisert, Paul
%A Ouchi, Hiroki
%A Inui, Kentaro
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F mim-etal-2019-unsupervised
%X Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers. In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation. Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.
%R 10.18653/v1/P19-2053
%U https://aclanthology.org/P19-2053
%U https://doi.org/10.18653/v1/P19-2053
%P 378-385

Markdown (Informal)

[Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring](https://aclanthology.org/P19-2053) (Mim et al., ACL 2019)

• Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring (Mim et al., ACL 2019)

ACL

• Farjana Sultana Mim, Naoya Inoue, Paul Reisert, Hiroki Ouchi, and Kentaro Inui. 2019. Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 378–385, Florence, Italy. Association for Computational Linguistics.