arxiv:2509.11498

DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification

Published on Sep 15

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Abstract

DeDisCo, using mt5 and Qwen models, achieves 71.28 macro-accuracy in discourse relation classification with augmented datasets and linguistic features.

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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.

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