Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features. (arXiv:1606.01990v1 [cs.CL])
Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features.