Pair-Aware Neural Sentence Modeling for Implicit Discourse Relation Classification

Implicit discourse relation recognition is an extremely challenging task, for it lacks of explicit connectives between two arguments. Currently, most methods to address this problem can be regarded as to solve it in two stages, the first is to extract features from two arguments separately, and the next is to apply those features to some standard classifier. However, during the first stage, those methods neglect the links between two arguments and thus are blind to find pair-specified clues at the very beginning. This paper therefore makes an attempt to model sentence with its targeted pair in mind. Concretely, an LSTM model with attention mechanism is adapted to accomplish this idea. Experiments on the benchmark dataset show that without the help of feature engineering or any external linguistic knowledge, our proposed model outperforms previous state-of-the-art systems.