Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory (2022)

Abstract: Emotional conversation systems generate responses for the input queries considering the speaker’s emotions in a conversation. Existing emotional conversation systems output emotional responses according to either a given emotion or the user’s emotion reflected in the input queries. Following a given emotion may lead to an emotional drift between the given emotion and the conversation state, and following only the user’s emotion may aggravate the user’s negative feelings if users suffer from a negative mood. In this paper, we propose to generate empathetic responses catering to the user’s emotions while leading the conversation to be emotionally positive. Particularly, by abstracting the conversation corpus, we extract and store the different responding strategies for different users’ emotions and conversational topics into a memory. We encourage positive emotions in conversation via a sentiment evaluator. We model the memory outputs with a Gaussian mixture distribution and sample a final responding strategy from the distribution. The strategy acts as a condition to a transformer model to generate responses. The experiments verify our model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.

## introduction most conversation models capture only the correlation between queries and responses and may overlook speaker's emotional states in the conversation. emotional conversation models @xcite consider speakers' emotions during the dialogues, where the speakers can be either users or chatbots. those models make chatbots aware of the user's emotions and enable them to respond empathetically. there are two directions for emotional conversation models. (1) controllable response generation enables chatbots figure 1 : an example of the differences among controllable response generation (in red), empathetic response generation (in blue), and our chatbot (in a mixture of red and blue). when responding to the sad news, blunt cheering up might lead to reinjury and a plain show of empathy tends to aggravate negative feelings. a suitable response should be both empathetic and positive. to respond conditioned on a certain emotion @xcite or style @xcite . those methods require an explicit emotion label as input and the label dominates the chatbot's response. (2) empathetic response generation, detects the user's emotion from the query so that the chatbots can respond empathetically considering the user's emotion @xcite . the above two directions cater to emotions in conversation but still possess some weaknesses. for the first direction, if the given emotion label mismatches with the user's emotion, controllable responses generation leads to an emotional drift @xcite among dialogues, that is, the emotions of query and response are inconsistent and incoherent. as the example shown in fig. 1 , if the user tells a sad story and the chatbot aggressively encourages the emotion to be happy, the chatbot's responses may be crude resulting in reinjury to the user. as for empathetic response generation, without acquiring any additional inductive bias, the user's emotion does not necessarily hint how to respond empathetically @xcite , so considering the user's emotion usually pushes chatbots to imitate the user's emotion. if the user is sad (fig. 1 ), simply imitating the user's emotion may aggravate the user's negative feelings. existing research hardly explores to balance empathy and positive emotions in response generation. to achieve that goal, we should adopt different responding strategies to different situations and finally lead positive conversations. for example, to respond to a sad story, we should first show our empathy and then gently lead a positive emotion; for a happy utterance, we can directly congratulate the user and even share other good news. in this paper, we propose a conversation model that generates empathetic responses and gently guides the mood of the conversation towards a positive direction. to customize responding strategies to different situations, we extract implicit responding strategies for specific emotions and topics by abstracting the training corpus. we store the extracted strategies into an emotion-specific queryresponse memory to assist the response generation. to lead positive emotions, we employ a sentiment evaluator to encourage positive responses. the above operations constrain the strategies and emotions in generation, thus they naturally decrease the diversity of the generated responses. to encourage diverse responses and fully utilize the memory outputs, inspired by conditional variational autoencoder (cvae) @xcite , we model the memory outputs with a gaussian mixture distribution and mix the memory outputs (i.e. responding strategies) by sampling a strategy vector z from the distribution. finally, the strategy vector z controls a conventional conversation model to generate responses. our experiments verify that our model not only exceeds the comparing methods in quality and diversify but also encourages positive conversation. our contributions are threefold: (1) we propose a conversation model to balance users' emotions and positive emotions. (2) we propose to abstract the corpus to build an emotion-specific query-response memory to carry responding strategies for different emotions and topics and propose a strategy mixer to fuse memory outputs. (3) our model surpasses some latest emotional chatbots in appropriateness, diversity, and positivity of responses. ## conclusion in this paper, we propose an emotional chatbot with the ability to make empathetic and emotionally positive responses. we construct an emotion-specific query-response memory to abstract and memorize the correlation between users' queries and the potential responses. the model fuses the memory outputs into a latent distribution and samples an implicit responding strategy from the distribution. the above operations are supervised by a sentiment evaluator to encourage positive emotions. our experiments show our model's strengths in appropriateness, diversity, and generation of positive responses. in the future, we will extend our model to multi-turn conversation scenarios.