Leveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via Mutual Learning Method

This paper presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English. With the absence of explicit discourse markers, traditional discourse techniques mainly concentrate on discrete linguistic features in this task, which always leads to data sparse problem. To relieve this problem, we propose a mutual learning neural model which makes use of multilevel semantic information together, including the distribution of implicit discourse relations, the semantics of arguments and the co-occurrence of words. During the training process, the predicted target of the model which is the probability of the discourse relation type, and the distributed representation of semantic components are learnt jointly and optimized mutually. The results of both binary and multiclass identification show that this method outperforms previous works since the mutual learning strategy can distinguish Expansion type from the others efficiently.