E mp DG : Multi-resolution Interactive Empathetic Dialogue Generation (2020)

Abstract: A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users’ expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model – EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.

## introduction studies on social psychology suggest that "empathy" is a crucial step towards a more humanized humanmachine conversation, which improves the emotional perceptivity in emotion-bonding social activities @xcite . to design an intelligent automatic dialogue system, it is important to make the chatbot become empathetic within dialogue interactions @xcite . therefore, in this paper, we focus on the task of empathetic dialogue generation @xcite , which automatically tracks and understands the user emotion information in a multi-turn dialogue scenario. despite the achieved successes @xcite , obstacles to establishing an empathetic conversational system are still far beyond current progress: (1) it is still difficult to accurately capture the nuances of human emotion in dialogue generation @xcite . (2) merely relying on the dialogue history but overlooking the potential of user feedback for the generated responses further aggravates the aforementioned deficiencies, which causes undesirable responses @xcite . in figure 1 , we give an example of the benchmark dataset empatheticdialogues @xcite . note that the emotional words in the sequence of utterances (in figure 1 , they are "new", "job" in utterance 1, "amazing", "excited" in utterance 2, and "excited" in utterance 3) have fine-grained emotional connections. without considering fine-grained emotional words, the responses generated by existing methods are trivial and uninformed, even though they expressed the appropriate emotions. therefore, explicitly modelling the fine-grained emotional factor is necessary. in this paper, we propose a multi-resolution adversarial empathetic dialogue generation model, named empdg, to address above challenges through generating more appropriate and empathetic responses. to capture the nuances of user emotions sufficiently, empdg generates responses by jointly taking coarsegrained dialogue-level emotions and fine-grained token-level emotions into account. the multi-resolution emotions are the prerequisites of the response generator to perceive the user's nuanced emotion states. furthermore, we propose an interactive adversarial learning framework to augment the user's feedback thoughtfully, where two interactive discriminators are designed to identify whether the generated responses evoke the emotion perceptivity regarding both the dialogue history and the user's emotions. conducted on a benchmark dataset empatheticdialogues, extensive experimental results have verified the effectiveness of the empdg in terms of both content quality and empathy quality. we also find that the empdg outperforms state-of-the-art baselines significantly. in general, our main contributions are summarized as follows: ## related work our research is in line with empathetic conversation generation through human-computer interactions, which avoids the additional step of assigning emotion to responses during conversations @xcite . several work @xcite @xcite @xcite @xcite @xcite @xcite have attempted to make dialogue models more empathetic. @xcite combine existing models in different ways to produce empathetic responses. @xcite softly combine the possible emotional responses from several separate experts to generate the final empathetic response. @xcite fine-tune a large-scale pre-trained language model with multiple objectives. however, all existing approaches only consider monogranular emotions in the dialogue context. in comparison, empdg jointly considers highly correlated coarse-grained emotional labels and fine-grained emotional terms. moreover, we explicitly consider the effect of user feedback via a novel interactive adversarial mechanism, so that empdg is capable to evoke more emotion perceptivity in dialogues. besides the advancements in empathetic dialogue models, the emergence of new emotion-labelled dialogue corpora, e.g., dailydialog @xcite and emotionlines @xcite , have also contributed to this research field. however, only 5% of the utterances in dailydialog and 16.68% of the utterances in emotionlines have diverse emotional labels and others are either "none" or "happy" labels. because of the extremely imbalanced data distribution, they are not suitable to be engaged as the benchmarks of empathetic dialogue generation. @xcite consider a richer and evenly distributed set of emotions and release a dataset empatheticdialogues, where a listener responds to a speaker who is under an emotional situation in an empathetic way. furthermore, several emotion lexicons @xcite have also been shown to be effective in tracking emotions in texts. in our work, we focus on the task of empathetic dialogue generation on the empatheticdialogues dataset and emotion lexicon (mohammad and turney, 2013). the second line of our related work is the emotional dialogue generation, which has received an increasing amount of attention to address emotion factors @xcite @xcite . prior studies on emotion-related conversational systems mainly focused on rule-based systems, which heavily rely on hand-crafted features @xcite . recently, many neural emotional dialogue generation approaches @xcite @xcite @xcite have been explored to control the emotional expression in the target response. however, as @xcite reveal that conventional emotional conversation systems aim to produce more emotion-rich responses according to a given specific user-input emotion, which inevitably leads to an emotional inconsistency problem. our research also aligns with recent advances in open-domain dialogue generation models @xcite @xcite @xcite . these dialogue models usually adopt the sequence-to-sequence (seq2seq) @xcite fashion. adversarial learning achieves considerable success in generating higher-quality responses @xcite but often leads to gradient vanishing as the discriminator saturates @xcite . to tackle this problem, @xcite utilize wasserstein gan @xcite to enhance response consistency with external facts. @xcite propose an adversarial decomposition method for fine-grained text representation. unlike the previous work, we investigate an adversarial approach to improve the empathy quality of neural dialogue models. ## problem formulation before detailing our method, we introduce our key notations and concepts. a multi-turn dialogue context consists of m utterances between two interlocutors. we assume both semantic context and emotional context exist in such dialogue. the semantic context u refers to the sequence of utterances, i.e., @xmath0, ..., u m ]. following lin (2019), we flat u into a long token sequence and insert a cls token at the start of the token sentence, i.e., @xmath1, x 1 , . . . , x m ]. the emotional context e considers emotions with different granularities, i.e., @xmath2, w 1 , ..., w e ], where w i is the emotional word in the semantic context u, lab is a special emotion token which is used to derive the emotional state of the dialogue context. we extract emotional words using an external emotional vocabulary v e (mohammad and turney, 2013). in the following part, we use x 0 to denote cls in u and w 0 to denote lab in e. given u and e, our model aims to generate a n-length response @xmath3, ..., y n } through maximizing the probability p (y|u, e) = n @xmath4, u, e). ## empdg in this section, we detail our proposed multi-resolutional adversarial model, abbreviated as empdg. the overview of empdg is illustrated in figure 2 . there are two main components in empdg: the empathetic generator and the interactive discriminators. to summarize, the empathetic generator is established based on an encoder-decoder architecture, which are all implemented with transformer @xcite . during encoding procedure, semantic context and the multi-resolution emotional context are encoded; whereas the decoder fuses the semantic context and emotional context to generate responses. to enhance the empathy of the generator, we design two cnn-based @xcite ) discriminators (i.e., the semantic discriminator and the emotional discriminator). in the training procedure, the two discriminators additionally interact with the user feedback (the next utterance and corresponding emotional words of the next utterance). by minimizing the wasserstein-1 distance to optimize the discriminators, we use the sum of classification results as a training signal to encourage response generator to evoke more emotion perceptivity. response generator (1) empathetic generator dialogue context encoder emotional discriminator user feedback semantic discriminator negative semantic dialogue context positive semantic semantic understanding multi-resolution emotion perception empathetic response decoder embedding layer emotion embedding negative emotion dialogue context positive emotion ## conclusion in this paper, we propose an multi-resolution interactive empathetic dialogue model (empdg) to evoke more emotion perceptivity in dialogue generation. two components are proposed to improve the performance of empathetic response generation. a multi-resolution empathetic generator combines coarsegrained dialogue-level and fine-grained token-level emotions to capture the nuanced emotional expressions in dialogue and evoke more emotion perceptivity. two interactive discriminators utilize user feedback as additional context to interact with the generated response and dialogue context to optimize the long-term goal of empathetic conversation generation. automatic and manual evaluation have shown that empdg can generate responses appropriate not only in content but also in empathy. there are several future directions for this setting. first, one of the potential extension of empdg would be incorporating it with external knowledge (e.g., user profile or commonsense knowledge) to help emotion perceptivity. second, in our setting, the emotional feedback and semantic feedback are separated. it is promising to model the interaction between semantic and emotional feedback. we leave these directions as future work.