Numerical Semantic Modeling for Implicit Discourse Relation Recognition

Abstract

Implicit discourse relation recognition (IDRR), which infers discourse logical relations without the help of explicit connectives, is the bottleneck of discourse parsing. It is also an effective mean to test how well the natural language understanding models grasp the logical semantics of the text. Unfortunately, as an important part of text logical semantics, numerical logic has not been paid any attention to in the community. In this work, we attach importance to numerical semantics and design a numerical logic reasoning module specifically for the numeric tokens in discourse arguments to enhance the discourse logic inferring. Graph neural network is utilized here to calculate the interactions of these numerical elements by self-attention and inter-attention according to their numerical type and their location in the discourse arguments. Experimental results show that our model outperforms the baseline 1.36 % F1 score on the PDTB2.0 dataset.