DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 77 references that are in the map. First posted on arXiv 15 Sep 2025.
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References · 14
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- 2018Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraphcited by 17
- 2021DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detectioncited by 15
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- 2019Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mappingcited by 5
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