Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy (2021)

Abstract: Emotion inference in multi-turn conversations aims to predict the participant’s emotion in the next upcoming turn without knowing the participant’s response yet, and is a necessary step for applications such as dialogue planning. However, it is a severe challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future. Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness. In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. In addition, we propose an ensemble strategy to further enhance the model performance. Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.

## introduction in this paper, we investigate the task of emotion inference in multi-turn conversations, which aims to explore how the conversation history affects the participant's future emotion, and predict the participant's emotion in the next upcoming turn, without knowing the participant's response yet. an example of the task is shown in figure 1 . emotion inference is a necessary step for applications such as dialogue planning, dialogue generation, and interpretability, among others @xcite @xcite . for example, in a human-machine conversation scenario, if a chatbot tries to say something to cheer you up when you feel down, then the chatbot must predict the emotional consequence first, and avoid words that may offend you or elicit negative emotion on you. previous studies on emotion analysis in conversations have mainly focused on recognizing the emotion of a given utterance, including bc-lstm @xcite , dialoguernn @xcite , dialoguegcn @xcite , cos-mic @xcite , etc., while the emotion inference task is to predict the emotion of the next upcoming utterance, in which the utterance in the next turn is not given. @xcite studied a similar task to the emotion inference, however they only took two turns as context and the multi-party multi-turn scenario was not considered. @xcite and @xcite estimate the sentiment polarity (negative or positive) of the next utterance, while our work anticipates the fine-grained emotion, such as happy, sad, angry, excited, and frustrated, etc. although extensive related work has been conducted, emotion inference in multi-turn conversations is still an understudied and challenging task, due to the lack of utterance information from the future and the complexity to capture the characteristics of emotional propagation in multi-turn conversations, such as persistence and contagiousness. to address these issues, an addressee-aware module is designed for both a sequence-based and a graph-based model to capture the propagation of emotional state in conversations and automatically learn whether the participant keeps the historical emotional state or is affected by others. in addition, we propose an ensemble strategy to further enhance the model performance. since the exact response of the participant in the next upcoming turn is unknown, there may be multiple potential emotional reactions. we run the models with different random seeds to generate multiple candidate results, and then train a fusion classifier 1) you liked it? you really liked it? the main novelty and contribution of this work is that we propose an addressee-aware module for the emotion inference task to model the emotional characteristics and anticipate the emotion trend in multi-turn conversations. moreover, an ensemble strategy is proposed to further enhance the model performance. the experiments on three benchmark conversational datasets show that our model achieves the new state-of-the-art f1 score. ## task definition given a multi-party multi-turn conversation history d along with the participants information, the emotion inference task aims to infer and anticipate the participant's emotion in the next upcoming turn. formally, conversation history @xmath0, p w 1 ), (u 2 , p w 2 ), • • • , (u m , p w m ), p a m+1 ] is a sequence of utterances, where u m is the utterance at time m and consists of n words, i.e., u @xmath1, w m 2 , • • • , w m n ), p w m is the writer/speaker of utterance u m at timestamp m. and p a m+1 is the addressee/listener in the next upcoming turn m + 1. the task is to infer the addressee p a m+1 's emotion e a m+1 at time m + 1 based on the utterances of previous m turns along with the participants information, without knowing the utterance information at time m + 1 yet: ## conclusion in this paper, we investigate the emotion inference in multi-turn conversations, which explores how the conversation history affects the participant's future emotion. to model the characteristics of emotion propagation in conversations: persistence and contagiousness, an addressee-aware module is designed for both a sequence-based and a graphbased model. in addition, an ensemble strategy is proposed to further enhance the model performance. the extensive experimental results on three benchmark datasets show that the proposed models achieve the new state-of-the-art f1 score, and the effectiveness of both the addressee-aware module and the ensemble strategy is demonstrated.