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This paper describes our submission to the DISRPT2021 Shared Task onDiscourse Unit Segmentation, Connective Detection, and Relation Classification.Our system, called DisCoDisCo, is a Transformer-based neural classifier whichenhances contextualized word embeddings (CWEs) with hand-crafted features,relying on tokenwise sequence tagging for discourse segmentation and connectivedetection, and a feature-rich, encoder-less sentence pair classifier forrelation classification. Our results for the first two tasks outperform SOTAscores from the previous 2019 shared task, and results on relationclassification suggest strong performance on the new 2021 benchmark. Ablationtests show that including features beyond CWEs are helpful for both tasks, anda partial evaluation of multiple pre-trained Transformer-based language modelsindicates that models pre-trained on the Next Sentence Prediction (NSP) taskare optimal for relation classification.

DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection