Authors:
Jieying Xue
;
Minh-Phuong Nguyen
and
Le-Minh Nguyen
Affiliation:
Japan Advanced Institute of Science and Technology, 923-1292, 1-8 Asahidai, Nomi, Ishikawa, Japan
Keyword(s):
Sentiment Analysis, Emotion Recognition in Conversation, COSMIC, Emotion Dependencies, Transformer.
Abstract:
Sentiment analysis, also called opinion mining, is a task of Natural Language Processing (NLP) that aims to extract sentiments and opinions from texts. Among them, emotion recognition in conversation (ERC) is becoming increasingly popular as a new research topic in natural language processing (NLP). The current state-of-the-art models focus on injecting prior knowledge via an external commonsense extractor or applying pre-trained language models to construct the utterance vector representation that is fused with the surrounding context in a conversation. However, these architectures treat the emotional states as sequential inputs, thus omitting the strong relationship between emotional states of discontinuous utterances, especially in long conversations. To solve this problem, we propose a new architecture, Long-range dependencY emotionS Model (LYSM) to generalize the dependencies between emotional states using the self-attention mechanism, which reinforces the emotion vector represe
ntations in the conversational encoder. Our intuition is that the emotional states in a conversation can be influenced or transferred across speakers and sentences, independent of the length of the conversation. Our experimental results show that our proposed architecture improves the baseline model and achieves competitive performance with state-of-the-art methods on four well-known benchmark datasets in this domain: IEMOCAP, DailyDialog, Emory NLP, and MELD. Our code is available at https://github.com/phuongnm94/erc-sentiment.
(More)