Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021)

Abstract: Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generation focus on the emotion flow in one direction, that is, from the context to response. We argue that conducting an empathetic conversation is a bidirectional process, where empathy occurs when the emotions of two interlocutors could converge on the same point, i.e., reaching an emotional consensus. Besides, we also find that the empathetic dialogue corpus is extremely limited, which further restricts the model performance. To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotional consensus and utilize some external unpaired data. Specifically, our model integrates a forward dialogue model, a backward dialogue model, and a discrete latent variable representing the emotional consensus into a unified architecture. Then, to alleviate the constraint of paired data, we extract unpaired emotional data from open-domain conversations and employ Dual-Emp to produce pseudo paired empathetic samples, which is more efficient and low-cost than the human annotation. Automatic and human evaluations demonstrate that our method outperforms competitive baselines in producing coherent and empathetic responses.

## introduction empathy, a fundamental trait of humans, describes the ability to place oneself in another person's position and share his/her feelings or emotions. besides, it has been considered to be one of the most valuable affective phenomena for improving human-machine interactions @xcite joint work with pattern recognition center, wechat ai, tencent inc, china. * xiaofang zhao is the corresponding author. figure 1 : an example of conducting an empathetic conversation. both responses show empathy to the speaker. @xcite ). the studies of empathy in natural language processing mainly include detecting empathy in spoken language or text @xcite , generating empathetic dialogue responses @xcite , and constructing empathy lexicons @xcite or datasets @xcite . the empathetic dialogue generation task has been regarded as a unidirectional process from the context to response, and is modeled as a multi-task learning that combines the emotion understanding and the emotion-enhanced response generation. therefore, existing work @xcite @xcite ) mainly focuses on improving the accuracy of emotion classification or enhancing response generation via integrating the detected emotion factor. conducting an empathetic conversation is naturally a bidirectional process: the speaker conveys his/her emotion by describing a certain situation, then the listener receives that emotion and feeds his/her feeling back to the listener via a response. then, the empathy is triggered when two interlocutors link similar experiences and their emotions could converge on the same point, i.e., reaching an emotion consensus. take the case in figure 1 as an example. the emotion consensus "sad" works as an intersection that connects both the speaker and listener, and it is a high-level abstraction behind the content, i.e., both two responses convey their acknowledgment of the sad feeling even with different expressions. therefore, a unidirectional model is not enough to model the relationship between the context and response. besides, previous models for this task only utilize paired data with limited capacity in a benchmark dataset, empatheticdia-logues. rather than manually annotating a larger empathetic dataset, we find that in open-domain conversations, there is large-scale emotional data that can be used to improve the performance. compared with recognizing whether a context-response pair is empathetic, obtaining either an emotional context or response (named as unpaired data in this paper) can be easier with a well-trained classifier. in this paper, we propose a dual-generative model for the empathetic dialogue generation task (dual-emp), which simultaneously constructs emotion consensus and utilizes unpaired data. dual-emp combines a forward dialogue model (generating a response based on its context) and a backward dialogue model (generating a context based on its responses) with a discrete latent variable. specifically, the forward and backward encoders convert the context and response into vectors at the same time, and then a discrete latent variable is used to capture the high-level emotion consensus shared in each context-response pair. moreover, the latent variable and an emotion-enhanced attention mechanism are integrated into both forward and backward decoders to better express proper empathy. to utilize unpaired emotional data, we firstly extract them from open-domain conversations with emotions. then we can get pseudo pairs by feeding either emotional responses or contexts to the backward or forward model. a joint training process is introduced to promote the semantic coherence between contexts and responses. furthermore, two types of optimization methods are applied to better train the entire model with paired and unpaired data. experimental results on a benchmark dataset empatheticdialogues show that dual-emp significantly outperforms competitive baselines in generating meaningful and related responses while expressing an appropriate empathy. our main contributions can be summarized as: (1) we point out that the empathetic dialogue generation contains bidirectional processes, and highlight the importance of constructing emotion consensus. besides, we propose a novel dualgenerative model that couples a forward and a backward dialogue model with a discrete latent variable capturing the shared emotion consensus. (2) we utilize unpaired emotional data to break the constraint of paired empathetic data in the widely-used benchmark dataset empatheticdialogues. (3) automatic and human evaluations show that our model outperforms competitive baselines in terms of fluency, coherence, and empathy. ## related work emotion-controllable response generation. infusing emotions into dialogue systems can make conversational agents more human-like and benefit the interactions between human and machine @xcite . emotioncontrollable response generation aims to generate emotional responses conditioning on a manuallyprovided label. existing work @xcite @xcite @xcite focused on obtaining responses that are not only meaningful, but also in accordance with the desired emotion. ## proposed method for empathetic dialogue generation, a dialogue consists of utterances from a speaker and a listener. given context @xmath0, l 1 , s 2 , l 2 ., ..., s t }, where ## experiments in this section, we conduct experiments to evaluate our proposed method. we firstly introduce some empirical settings. then we illustrate our results on both automatic and human evaluations. finally, we show some cases generated by different models and do further analyses over our method. ## conclusion and future work in this paper, we propose a dual-generative model, dual-emp, to generate the empathetic response given a context. we point out that conducting an empathetic conversation is a bidirectional process, and empathy is mainly reflected by emotion consensus between the context and response. then we couple forward and backward dialogue models with a discrete latent variable denoting the emotion consensus. moreover, we integrate unpaired emotional data from open-domain conversations into dual-emp to relieve the need of paired data. experimental results on a benchmark dataset show that dual-emp can generate fluent, related, informative, and empathetic responses. as the future work, we will prove the effectiveness of our method based on pre-trained models, and analyze how classification errors in unpaired data affect the generation.