Affective Decoding for Empathetic Response Generation (2021)

Abstract: Understanding speaker’s feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.

## introduction endowing a dialogue system with the ability of empathy responding has attracted a growing body of research @xcite and is believed to be crucial for many service-oriented applications, such as mental health interventions @xcite , assisting medical diagnosis @xcite , and restaurant/hotel booking services @xcite @xcite . being empathetic requires one to be able to understand the implied feeling of the conversation partner, or in other words, to place oneself in partner's position. therefore, to produce proper responses, an empathetic dialogue system (eds) needs to understand not only the situation of the speaker 1 and the causes @xcite , but also the emotion state of speaker. in @xcite formally introduced the task for dialogue systems to respond i there, don't know what to do, just broke up with my girlfriend, we were 8 years together. sorry to hear! do you have any idea about the breakup? did you think about it? speaker listener emotion: lonely i have felt some of the best moments of pure happiness seeing someone i love getting something they've worked hard for --like my daughter's acceptance into her college and major of choice! that's exciting! congratulations to her. i bet there was lots of celebration to be had for such an achievement! speaker listener emotion: joyful figure 1 : two example dialogue sessions from the em-pdial dataset with the emotion lonely and joyful, repsectively. to conversations with emotions. they also constructed a benchmark corpus called empathet-icdialogues (abbreviated as empdial), which consists of conversations with a wide range of emotion states for task evaluation. figure 1 shows an example session of a dialogue from empdial, where the situation reflects the emotion state of lonely and joyful. several approaches have been proposed for modelling emotions, which is a key challenge for building an eds. these approaches follow two main enterprises: one is multi-task learning @xcite , which trains models for both dialogue generation and predicting the emotion of the dialogue; the other enforces the model to generate empathetic responses conditioning on the emotion state predicted from the dialogue context with a pre-trained emotion classifier (rashkin et al., 2019). our work follows a similar vein to the second enterprise, where we propose a simple yet efficient technique coined affective decoding (ad), which can effectively incorporate emotion signals into model training and generate more empathetic responses. our method can work in two different modes. the first mode injects emotion embedding in each decoding step. this is different to rashkin et al. ( 2019 ) who only applied prepending at the first time-step on the encoder. for the second mode, we introduce an additional auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. in addition, we systematically evaluate and compare ad with the existing mainstream emotion modelling methods for empathetic responding, including both prepending emotion embeddings @xcite and multi-task learning @xcite . based on the empdial dataset, we conducted comprehensive empirical studies, which include automatic (e.g., bleu, bow) and two human evaluations. for human evaluation, we assess both model-level performance and finer-grained level aspects concerning empathy, relevance, and fluency of the generated responses. empirical results show the effectiveness of our affective decoding and that our model with the auxiliary dual emotion encoder works the best. while rashkin et al. (2019) reported that multi-task learning did not provide consistent improvements for the task, we actually found multi-task learning performs even worse than a pre-trained language model @xcite fine-turned on the empdial dataset. to summarise, our contributions are 3-fold: (1) we introduce a simple yet efficient decoding method called affective decoding to the task of empathetic response generation; (2) we conduct a comprehensive comparison between various emotion modelling methods in empathetic dialogue modelling by means of automatic evaluation and 2 human evaluations; the rest of the paper is organised as follows. §2 presents our model for empathetic response generation. we show the experimental setup and results in §3. §4 presents some case studies and finally §5 concludes the paper. the code is available at: https://github.com/zenggo/ affective-decoding-4-empathetic-dialog . ## methodology in this section, we describe our affective decoding model, which consists of two key components, namely, a pre-trained response generator, transfo @xcite , and the affective decoder for enhancing empathetic responding. ## experiment we evaluate our models on empdial, using the original split of @xcite and their emotion classifier based on fasttext @xcite . in terms of our own models, apart from ad and ad+de, we also further tested a model variant (adm), which considers multi-task learning. we detail each of our model variants below. ## conclusion in this paper, we propose a simple and effective technique called affective decoding for empathetic response generation. empirical results based on extensive human evaluation show that our models (ad and ad+de) outperform several strong baselines. simply fine-tune the pre-trained transfo on empdial achieves decent performance. mtl, [joyful] s: my mother just got a promotion at her job ! l: that 's great ! what kind of job is it ? s: it 's a financial analyst job ! l: that 's great ! i 'm sure you 're proud of her ! [disappointed] s: my mother was diagnosed with pan cre atic cancer a few weeks ago . l: oh no ! i 'm sorry to hear that . is she going to be okay ? s: i think so , but i was n't expecting it at all . l: i 'm sorry to hear that . i hope everything works out for you . which has been used in some eds, shows negative effects on the overall performance. as a side outcome, we also confirm the low validity of the mainstream automatic metrics for evaluating empathetic dialogue systems. it was noted that empathetic dialogue systems tend to generate generic responses such as "i'm sorry to hear that.". therefore, one important future work is to improve the diversity and informativeness of the empathetic responses generated by an eds. one possible technical direction is to employ variational autoenoders @xcite @xcite , which have been shown effective in improving the diversity in response generation.