Sentiment- Emotion- and Context-Guided Knowledge Selection Framework for Emotion Recognition in Conversations

Abstract

Emotion recognition in conversations (ERC) needs to detect the emotion of each utterance in conversations. However, it is difficult for machines to recognize the emotion of utterances like humans, partly because of the lack of commonsense knowledge. Despite existing efforts gradually incorporate knowledge in ERC, they can not adaptively adjust knowledge according to different utterances and their context. In this paper, we propose a knowledge selection framework SKSEC ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> elect <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge in light of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> entiment <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> motion and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontext). In the SKSEC framework, firstly, external knowledge is eliminated by three Knowledge Elimination (KE) modules. More concretely, In word-level KE, the concept knowledge different from the sentiment corresponding to the word in utterances is randomly eliminated. In utterance- or context-level KE, If the similarity between the knowledge representation and the emotion label representation of the current utterance or its context is less than the preset threshold, the knowledge will be eliminated. Then we refine the weight of knowledge using two Graph ATtention (GAT) mechanisms. Specifically, In Sentics GAT, we employ a dimensional emotion model to measure words in utterances and their corresponding knowledge and adjust the weight of knowledge according to their emotional similarity. In Semantics GAT, the weight of knowledge is adjusted according to the semantic similarity between context and incorporated knowledge. Finally, we feed the selected knowledge to the most advanced models to evaluate the quality of knowledge. The experimental results show that the SKSEC framework can effectively improve the performance of the model by eliminating and refining external knowledge in different size and domain datasets.