Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation (2021)

Abstract: For a computer to naturally interact with a human, it needs to be human-like. In this paper, we propose a neural response generation model with multi-task learning of generation and classification, focusing on emotion. Our model based on BART (Lewis et al., 2020), a pre-trained transformer encoder-decoder model, is trained to generate responses and recognize emotions simultaneously. Furthermore, we weight the losses for the tasks to control the update of parameters. Automatic evaluations and crowdsourced manual evaluations show that the proposed model makes generated responses more emotionally aware.

## introduction the performance of machine translation and summarization has been approaching a near-human level in virtue of pre-trained encoder-decoder models, such as bart @xcite and t5 @xcite . the same technology has been applied to dialogue systems, which are now expected to be put to practical use. to interact naturally with a human, the computer needs to be human-like. several methods have been proposed to build such dialogue systems. they include a system interacting based on knowledge and common sense @xcite and that interacting by considering one's own and the other's personality @xcite . in particular, we focus on the viewpoint of emotion as targeted in @xcite . in this paper, we propose a multi-task learning method for building a dialogue system that takes the speaker's emotions into account. also, we focus on the hierarchy of emotions @xcite and simultaneously train multiple emotion recognition tasks with different granularity. our multi-task learning model is not expected to share complementary information among similar tasks as previous work @xcite , and we do not aim at improving the accuracy of emotion recognition. instead, we focus on generating emotion-aware responses. also, concerned that the ratio of emotion recognition in multi-task learning is too large, we explore further quality improvement by weighting each loss. we build a model based on bart @xcite , a pre-trained transformer @xcite model, to implement multi-task learning of response generation and emotion recognition. experiments are performed using a dialogue corpus without context. the effectiveness of the proposed method in generating responses is confirmed by automatic and manual evaluations. multi-task learning of response generation and emotion recognition makes generated responses more emotionally aware of utterances. the improvement is not only on the emotional aspect but also on the quality of fluency, informativeness, and relevance. we also found that controlling the parameters by weighting the losses improved the performance of the model. ## related work one of the previous studies on emotion-based response generation is the emotional chatting machine (ecm) @xcite . ecm is used together with an emotion classifier to generate a response based on a given emotion. emptransfo (zandie and mahoor, 2020) is a similar model to ours. given an utterance, a model based on gpt @xcite learns an emotion and an action simultaneously in addition to a response, which improves the quality of generated responses. these models focus on the emotion of a response so that they do not generate a response based on the emotion of an utterance. @xcite incorporate an emotion encoder into a hierarchical seq2seq architecture, enabling a system to understand the emotional context on a user. tg-eacm @xcite , the successor of eacm @xcite , is a model that considers not only the emotion in an utterance but also the emotion that a response should have. the model learns a distribution to infer both the emotion of the utterance and the response from a given utterance. care @xcite uses some commonsense to generate a response with both rationality and emotion. through latent concepts obtained from an emotionally aware knowledge graph, predicted responses can be emotional and rational. actually, the above models require separate units or special architecture for understanding emotion in a dialogue. in contrast, our proposed model achieves that with a single structure, inherited from transformer @xcite and bart @xcite . in other words, our model does not need an extra unit. therefore, the proposed method consequently reduces the redundancy of transformer parameters @xcite and realizes more efficient understanding of emotion to generate a response. ## conclusion we worked on improving the quality of neural network-based response generation. focusing on the aspect of emotion, we proposed a multi-task learning response generation model that includes the tasks of generation and classification. through automatic and manual evaluations, we confirmed that the proposed model improved several metrics of performance. moreover, we further improved the quality of the model by weighting losses. as a result, we found that such weighting improved