{"ID":2870689,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11498","arxiv_id":"2509.11498","title":"DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification","abstract":"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.","short_abstract":"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 la...","url_abs":"https://arxiv.org/abs/2509.11498","url_pdf":"https://arxiv.org/pdf/2509.11498v4","authors":"[\"Zhuoxuan Ju\",\"Jingni Wu\",\"Abhishek Purushothama\",\"Amir Zeldes\"]","published":"2025-09-15T01:25:37Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}