DC Field | Value | Language |
---|---|---|
dc.contributor.author | LEE, GARY GEUNBAE | - |
dc.contributor.author | Do, Heejin | - |
dc.contributor.author | Kim, Yunsu | - |
dc.date.accessioned | 2024-03-06T05:23:16Z | - |
dc.date.available | 2024-03-06T05:23:16Z | - |
dc.date.created | 2024-02-20 | - |
dc.date.issued | 2023-07-09 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/121436 | - |
dc.description.abstract | Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. However, such settings conflict with real-education situations; pre-graded essays for a particular prompt are lacking, and detailed trait scores of sub-rubrics are required. Thus, predicting various trait scores of unseen-prompt essays (called cross-prompt essay trait scoring) is a remaining challenge of AES. In this paper, we propose a robust model: prompt- and trait relation-aware cross-prompt essay trait scorer. We encode prompt-aware essay representation by essay-prompt attention and utilizing the topic-coherence feature extracted by the topic-modeling mechanism without access to labeled data; therefore, our model considers the prompt adherence of an essay, even in a cross-prompt setting. To facilitate multi-trait scoring, we design trait-similarity loss that encapsulates the correlations of traits. Experiments prove the efficacy of our model, showing state-of-the-art results for all prompts and traits. Significant improvements in low-resource-prompt and inferior traits further indicate our model's strength. | - |
dc.language | English | - |
dc.publisher | Association for Computational Linguistics (ACL) | - |
dc.relation.isPartOf | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 | - |
dc.relation.isPartOf | Proceedings of the Annual Meeting of the Association for Computational Linguistics | - |
dc.title | Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023, pp.1538 - 1551 | - |
dc.citation.conferenceDate | 2023-07-09 | - |
dc.citation.conferencePlace | CN | - |
dc.citation.endPage | 1551 | - |
dc.citation.startPage | 1538 | - |
dc.citation.title | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 | - |
dc.contributor.affiliatedAuthor | LEE, GARY GEUNBAE | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
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