  Humans comprehend the meanings and relations of discourses heavily relying on
their semantic memory that encodes general knowledge about concepts and facts.
Inspired by this, we propose a neural recognizer for implicit discourse
relation analysis, which builds upon a semantic memory that stores knowledge in
a distributed fashion. We refer to this recognizer as SeMDER. Starting from
word embeddings of discourse arguments, SeMDER employs a shallow encoder to
generate a distributed surface representation for a discourse. A semantic
encoder with attention to the semantic memory matrix is further established
over surface representations. It is able to retrieve a deep semantic meaning
representation for the discourse from the memory. Using the surface and
semantic representations as input, SeMDER finally predicts implicit discourse
relations via a neural recognizer. Experiments on the benchmark data set show
that SeMDER benefits from the semantic memory and achieves substantial
improvements of 2.56\% on average over current state-of-the-art baselines in
terms of F1-score.
