Multi-Channel CNN Based Inner-Attention for Compound Sentence Relation Classification

Relation classification is a vital task in natural language processing, and it is screening for semantic relation between clauses in texts. This paper describes a study of relation classification on Chinese compound sentences without connectives. There exists an implicit relation in a compound sentence without connectives, which makes it difficult to realize the recognition of relation. The major challenges that relation classification modeling faces are how to obtain the contextual representation of sentence and relation dependence features between clauses. To solve this problem, we propose a novel Inatt-MCNN model to extract sentence features and classify relations by combining multi-channel CNN and Inner-attention mechanism. This network structure utilizes CNN to extract local features of sentences and Inner-attention to capture sentence-level feature representations for this relation classification task. Besides, since the Inner-attention is based on Bi-LSTM, the global and long-term dependence semantic information can be well obtained in Inatt-MCNN to promote the model performance. We conduct experiments on two public Chinese discourse datasets: the Chinese compound sentence corpus (CCCS) dataset and the Tsinghua Chinese Treebank(TCT) dataset. Compared with the previous public methods, Inatt-MCNN model has superior performance and achieves the highest accuracy, especially on the CCCS dataset.