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Autori principali: Ju, Zhuoxuan, Wu, Jingni, Purushothama, Abhishek, Zeldes, Amir
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2509.11498
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author Ju, Zhuoxuan
Wu, Jingni
Purushothama, Abhishek
Zeldes, Amir
author_facet Ju, Zhuoxuan
Wu, Jingni
Purushothama, Abhishek
Zeldes, Amir
contents This paper presents DeDisCo, Georgetown University's entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.
format Preprint
id arxiv_https___arxiv_org_abs_2509_11498
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
Ju, Zhuoxuan
Wu, Jingni
Purushothama, Abhishek
Zeldes, Amir
Computation and Language
This paper presents DeDisCo, Georgetown University's entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.
title DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
topic Computation and Language
url https://arxiv.org/abs/2509.11498