Causality Existence Classification from Multilingual Texts Using End-to-End LSTM Models

In this study, we propose a neural model for extracting causal sentences from both English and Japanese documents. Causal knowledge extraction is an important topic in the area of natural language processing. However, numerous studies concerning the extraction of causal knowledge target only one language. Therefore, in this study, we propose a multilingual model for extracting causal knowledge. Our model employs end-to-end architecture to deal with multilingual documents by using one model. Through the experiment, the effectiveness of our model is revealed.