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Humans comprehend the meanings and relations of discourses heavily relying ontheir semantic memory that encodes general knowledge about concepts and facts.Inspired by this, we propose a neural recognizer for implicit discourserelation analysis, which builds upon a semantic memory that stores knowledge ina distributed fashion. We refer to this recognizer as SeMDER. Starting fromword embeddings of discourse arguments, SeMDER employs a shallow encoder togenerate a distributed surface representation for a discourse. A semanticencoder with attention to the semantic memory matrix is further establishedover surface representations. It is able to retrieve a deep semantic meaningrepresentation for the discourse from the memory. Using the surface andsemantic representations as input, SeMDER finally predicts implicit discourserelations via a neural recognizer. Experiments on the benchmark data set showthat SeMDER benefits from the semantic memory and achieves substantialimprovements of 2.56\% on average over current state-of-the-art baselines interms of F1-score.

Neural Discourse Relation Recognition with Semantic Memory