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Implicit discourse relation classification is one of the most difficult partsin shallow discourse parsing as the relation prediction without explicitconnectives requires the language understanding at both the text span level andthe sentence level. Previous studies mainly focus on the interactions betweentwo arguments. We argue that a powerful contextualized representation module, abilateral multi-perspective matching module, and a global information fusionmodule are all important to implicit discourse analysis. We propose a novelmodel to combine these modules together. Extensive experiments show that ourproposed model outperforms BERT and other state-of-the-art systems on the PDTBdataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze theeffectiveness of different modules in the implicit discourse relationclassification task and demonstrate how different levels of representationlearning can affect the results.

On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification