TY - GEN
T1 - Unsupervised learning of discourse-aware text representation for essay scoring
AU - Mim, Farjana Sultana
AU - Inoue, Naoya
AU - Reisert, Paul
AU - Ouchi, Hiroki
AU - Inui, Kentaro
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR1513 and JSPS KAKENHI Grant Number 19K20332. We would like to thank the anonymous ACL reviewers for their insightful comments. We also thank Ekaterina Kochmar for her profound and useful feedback.
Publisher Copyright:
© 2019 Association for Computational Linguistics.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85083999157
T3 - ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop
SP - 378
EP - 385
BT - ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 - Student Research Workshop, SRW 2019
Y2 - 28 July 2019 through 2 August 2019
ER -