Multi-Task Emotion Cause Pair Extraction Based on Context and Semantic Modal
To consider more modal information, contextual and semantic features are modeled in detail, and emotion cause pair extraction is carried out on the fusion of two modal features.For the contextual modality, a clause embedding method is employed to obtain representations of emotions and causes,and dual-factor attention mechanism is utilized to derive a global contextual matrix.In the mean time, by constructing a graph neural network for inter-clause semantics, local semantic features are obtained.Finally, the fusion features are obtained by the main and auxiliary mode matching method for multi-task prediction, including emotional sentence, cause sentence and emotion-cause pair extraction task.Experimental results indicate that when extracting classic Chinese emotion-cause pairs, compared to the best baseline system, the F-measure has improved by 2.2％.
emotion cause pair extractionglobal contextlocal semanticsmodal matching
NETL
NSTL
万方数据
