Empathetic Dialog Generation with Fine-Grained Intents (2021)

Abstract: Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on pre-defined emotion labels to guide the response generation, or use deterministic rules to decide the emotion of the response. With the advent of advanced language models, it is possible to learn subtle interactions directly from the dataset, providing that the emotion categories offer sufficient nuances and other non-emotional but emotional regulating intents are included. In this paper, we describe how to incorporate a taxonomy of 32 emotion categories and 8 additional emotion regulating intents to succeed the task of empathetic response generation. To facilitate the training, we also curated a large-scale emotional dialog dataset from movie subtitles. Through a carefully designed crowdsourcing experiment, we evaluated and demonstrated how our model produces more empathetic dialogs compared with its baselines.

## introduction empathy is considered to be an innate ability of human beings @xcite and plays an important role in people's social communication @xcite . it has been shown that integrating empathy into dialog systems could improve user experience for human-computer interaction @xcite . one of the empathetic components is the capacity to respond with an appropriate emotion to another person's mental states @xcite . in this regard, many existing neural dialog systems @xcite @xcite @xcite ) generate emotional responses conditioned on a pre-specified emotion label. however, this might be impractical when deploying the chatbots in reality, since an extra label is required as input. other neural dialog systems @xcite @xcite @xcite adopt manually defined rules, either explicitly or implicitly, to decide the emotion state for the response to be generated, e.g., following/reversing the speaker's emotion, or just maximizing the emotion content in the response. however, such deterministic rules are not confirmed by psychology literature, and they ignore the subtle interactions captured in human conversations, where the listener often exhibits empathetic intents that are more neutral. figure 1 gives an example of a situation where responding with the same or opposite emotion fails to drive the con-versation towards an empathetic direction. in fact, as revealed by @xcite , listeners are much more likely to respond with questioning to sad or angry emotions of another person, than expressing similar or opposite emotions. therefore, it is necessary to incorporate these additional empathetic response intents explicitly into the design of dialog systems. existing neural dialog systems adopt an empathetic dialog dataset that either has no neutral category @xcite , or the neutral category is a conglomerate of intents that cannot be clearly defined. this is why this category is often called other, which shows it is not sufficiently treated @xcite . while @xcite proposed to model open-domain dialog generation as the selection of dialog acts that control the generation of responses, they did not specifically focus on the generation of empathetic dialogs. based on the taxonomy proposed by @xcite , we incorporated an extra set of eight empathetic response intents (questioning, agreeing, acknowledging, sympathizing, encouraging, consoling, suggesting, and wishing) plus neutral into the design of an empathetic dialog model, in addition to the 32 emotion categories proposed by @xcite . emotional experience is primarily a reaction to an external event, for example, a loud sound, a surprising result on an exam, etc. in the case of dialogs, this emotional experience is shared by the interlocutors. when a listener wants to acknowledge or console the speaker, for example, he or she is expressing an emotional intent. this is the reason we treat all of these additional categories as well as the 32 emotion categories as dialog intents. overall speaking, our contributions are as follows: (1) we are the first to consider modeling a fine-grained set of empathetic response intents in an empathetic dialog model, which ensures a more precise learning of the emotional interactions revealed in the dialog data; (2) to facilitate the training of our empathetic dialog model, we curated a large-scale dialog dataset from movie subtitles; (3) to effectively evaluate our empathetic dialog model, we carefully designed a crowdsourcing experiment that enabled the workers to work on the tasks more easily. a total number of 6,000 dialogs were evaluated, which, to our knowledge, has never been attempted before for the evaluation of empathetic dialog systems. ## related work neural dialog generation dialog generation has been treated as a sequence transduction problem since the advent of deep neural models. @xcite trained the seq2seq network on it helpdesk dialogs and opensubtitles data. @xcite applied an attention mechanism to the seq2seq network and trained it on short-text social media dialogs. to adapt the seq2seq model to a multi-turn setting, @xcite designed a hierarchical encoder-decoder structure, based on which @xcite devised a hierarchical attention mechanism so that the model could pay attention at both token-level and utterance-level. ## data curation existing empathetic dialog corpora are usually limited in size and training solely on these datasets could not give us a chatbot with desirable performance. therefore, we would like to take advantage of transfer learning and pre-train the dialog model on a huge amount of dialog data (not necessarily empathetic), and then fine-tune it on a possibly much smaller empathetic dialog dataset. ## an empathetic dialog model we propose an empathetic dialog model that incorporates the fine-grained set of empathetic response intents, by training a classifier that predicts the response emotion/intent, and based on that, generates the response accordingly. compared with our previous multi-turn emotionally engaging dialog model (meed) @xcite , this model has more fine-grained emotions/intents, and allows for more controllability over the generated responses. the problem could be defined as follows: given a dialog context x consisting of one or more utterances u 1 , u 2 , . . . , u m , spoken between two people, try to generate a response ŷ that not only follows the dialog context but also is emotionally appropriate. our model consists of three modules: (1) an encoder responsible for encoding the input x into vector representations; (2) a response emotion/intent predictor which takes x as input and decides in which emotion/intent the model should respond; (3) a decoder responsible for generating the actual response. we use transformer @xcite encoder structure for our encoder and emotion/intent predictor, and transformer decoder structure for our decoder. figure 3a gives an overall depiction of the whole model architecture. all the three modules have the same input representation, which we describe in detail next. ## evaluation we trained our empathetic dialog model and the baselines on three datasets and evaluated them in held-out setting (meaning the test data comes from the same domain as the training data) and zero-shot setting (meaning the test data comes from a different domain than the training data), using both automatic metrics and human judgement via crowdsourcing. ## conclusion in this paper, we emphasize the importance of incorporating more fine-grained empathetic response intents into the design of empathetic dialog models. to this end, we proposed an empathetic dialog model capable of learning the emotion/intent interactions from the dialog data at a more precise level, and producing empathetic responses accordingly. to facilitate the training process, we also curated a large-scale dialog dataset from the open-subtitles corpus. pre-training dialog models on this dataset could largely boost the performance of down-stream empathetic response generation. our model was evaluated through a carefully designed human evaluation experiment on the crowdsourcing platform, on a large test set never attempted before. as future work, we would like to improve the accuracy of the response emotion/intent predictor in the model, which we found plays a vital role in generating empathetic responses.