Episodic Memory Network with Self-attention for Emotion Detection

Abstract

Accurate perception of emotion from natural language text is key factors to the success of understanding what a person is expressing. In this paper, we propose an episodic memory network model with self-attention mechanism, which is expected to reflect an aspect, or component of the emotion sementics for given sentence. The self-attention allows extracting different aspects of the input text into multiple vector representation and the episodic memory aims to retrieve the information to answer the emotion category. We evaluate our approach on emotion detection and obtains state-of-the-art results comparison with baselines on pre-trained word embeddings without external knowledge.