Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection (2022)

Abstract: Empathy, which is widely used in psychological counseling, is a key trait of everyday human conversations. Equipped with commonsense knowledge, current approaches to empathetic response generation focus on capturing implicit emotion within dialogue context, where the emotions are treated as a static variable throughout the conversations. However, emotions change dynamically between utterances, which makes previous works difficult to perceive the emotion flow and predict the correct emotion of the target response, leading to inappropriate response. Furthermore, simply importing commonsense knowledge without harmonization may trigger the conflicts between knowledge and emotion, which confuse the model to choose the correct information to guide the generation process. To address the above problems, we propose a Serial Encoding and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation. We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response. Besides, we design a novel framework to model the interaction between knowledge and emotion to solve the conflicts generate more sensible response. Extensive experiments on the utterance-level annotated EMPATHETICDIALOGUES demonstrate that SEEK outperforms the strong baseline in both automatic and manual evaluations.

## introduction enriching dialogue systems with human characteristics and capabilities is a hotspot in the humanlike dialogue system research area. empathy, which is used extensively in psychological counselling @xcite @xcite , is a key trait of everyday human conversations. in contrast to generating responses with the first case shows the speaker's emotion went from fear at the beginning of the conversation to an embarrassed self-deprecation, ending with a happy mood. and the second case shows that cem chooses the wrong knowledge leading to inappropriate response. to this end, we propose a serial encoding and emotion-knowledge interaction (seek) method for empathetic dialogue generation. to achieve a more fine-grained perception of emotional dynamics, we use an utterance-level encoding strategy which is more sensitive to the emotion flow in the conversations and able to predict the emotion characteristic of the response. we further introduce two new emotion-intent identification tasks to understand contextual emotion and predict the emotional and intentional trait of responses. for the problem of conflicts between knowledge and emotions, we also design a framework modeling the process of bi-directional interaction between them. extensive experimental results on the utterance-level annotated empatheticdialogues (ed) dataset (welivita and pu, 2020) demonstrate that seek outperforms the strong baseline with both automatic and manual evaluation metrics. our contributions are summarized as follows: in order to control the emotion of the generated response, which is one of the fundamental characteristics of daily conversation, plenty of approaches @xcite @xcite @xcite view the target emotion as a guiding information of the models' generator. contrary to controlling the emotion of the target response, the task of empathetic dialogue generation requires that the models learn a proper emotion to express empathy. numerous researchers have attempted to improve the dialogue models' ability to respond empathetically. @xcite proposed a benchmark and dataset to build and evaluate empathetic dialogue generation models. @xcite learned a precise emotion distribution of the response based on mixture of experts. @xcite split the emotions into two classes and designed a framework to mimic the target emotion in a certain class. @xcite utilized user feedback to build a multi-resolution adversarial training framework. in addition, @xcite and @xcite focused on the keywords and emotion cause of dialogue history to better understand the context-level emotion and recognize feature transitions between utterances. as well, several datasets @xcite of empathetic dialogue generation have been published for further research. however, most of the current approaches do not pay enough attention to the emotion flow of the conversations. commonsense knowledge is widely used to build dialogue systems. @xcite utilize commonsense knowledge graph to gain candidate words for generation. @xcite adopt comet @xcite , a pre-trained language model to generate commonsense inference for retrieving implicit information of dialogue context. in addition, @xcite construct a graphbased framework to encode the context-knowledge graph retrieved on commonsense knowledge base. the knowledge introduced into these models might become a trigger of logical conflicts due to the absence of harmony selection. ## conclusion in this paper, we study the task of empathetic dialogue generation. the strong baselines ignore emotion flow of the conversations. we therefore proposed a serial encoding and emotion-knowledge interaction (seek) method for empathetic dialogue generation, to predict the correct emotion of the target response by perceiving the emotion flow of the context and harmonizing commonsense knowledge with fine-grained emotions to avoid conflicts. experiments on the utterance-level annotated em-patheticdialogues show that our model outperforms the baselines, and the ablation studies indicate that all the components of our model, the encoding strategy, and the commonsense knowledge work. in the future, we will focus on further usage (e.g. providing online-emotion aid) of empathetic systems and try to improve normalization capabilities of our model on other datasets.