An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020)

Abstract: Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.

## introduction emotion recognition in conversations (erc) aims to recognize the emotion of each utterance in conversations. recently it has received much attention due to its applications in various conversation scenes, such as emotional chatbots @xcite , emotion detection of customers in artificial services @xcite , sentiment analysis of comments in social media @xcite , and so on. different from the common sentence-level emotion recognition task, erc is special due to some characteristics. the first one is that the utterances are context dependent, and modeling context can provide more information for emotion recognition @xcite . the second characteristic of erc is that the utterances are speaker-sensitive, thus many researchers modeled the state of speakers and the inter-speaker dependency relations @xcite @xcite . in this paper, we observe another characteristic that is the emotions of the utterances are interactive. for example, in figure 1 , the emotion of the utterance from speaker a can directly influence speaker b. thus, modeling the emotion interaction between utterances is helpful for the erc task. previous works usually implicitly model the emotion interaction by modeling dialogue context @xcite . however, because of the arbitrariness of the dialogue, the context utterances often convey misleading emotion information when recognizing the emotion of the target utterance, such as in figure 1 (a). to solve this problem, we observe that the gold emotion labels (such as "happy", "angry") of the context utterances can provide explicit and accurate emotion interaction between utterances, such as in figure 1 @xcite . thus, we can introduce the emotion labels to explicitly model the emotion interaction between utterances. however, a challenging problem is this approach requires inputting gold labels of context utterances, which is impossible at inference time. we observe a phenomenon, the utterances which are helpful for the emotion recognition of the target utterance are usually near the target utterance and the number is ## method in this section, we introduce our proposed iterative emotion interaction network as shown in figure 2 . our network consists of three components: an utterance encoder, an emotion interaction based context encoder, and iterative improvement mechanism. the utterance encoder is used to obtain the representations of all utterances in a conversation. the emotion interaction based context encoder introduces the emotion probabilities of the utterances and integrates them and the utterance representations to explicitly model the emotion interaction. the iterative improvement mechanism contains initial emotion prediction, iterative emotion feedback and loss for iteration, which combines the above two encoders to iteratively improve the emotion predictions. in the following sections, we describe these components in detail. emotion classifier concat emotion classifier concat ec-encoder (@xmath0... ... initial emotion prediction emotion classifier 1 2 attention layer input dialogue bigru bigru bigru emotion embedding emotion embedding emotion probabilities u u emotion probabilities u e 0 e t-1