Eliciting Rich Positive Emotions in Dialogue Generation (2023)

Abstract: Positive emotion elicitation aims at evoking positive emotion states in human users in open-domain dialogue generation. However, most work focuses on inducing a single-dimension of positive sentiment using human annotated datasets, which limits the scale of the training dataset. In this paper, we propose to model various emotions in large unannotated conversations, such as joy, trust and anticipation, by leveraging a latent variable to control the emotional intention of the response. Our proposed emotion-eliciting-Conditional-Variational-AutoEncoder (EE-CVAE) model generates more diverse and emotionally-intelligent responses compared to single-dimension baseline models in human evaluation.

## introduction in human communication theory, intentionality (intention of speakers) and effectiveness (effects of conversations) are key factors to a conversation @xcite @xcite , both of which can be exhibited by emotions @xcite . there has been research on dialogue systems for generating human-like, emotionally intelligent responses @xcite @xcite . however, existing work focuses on generating utterances with targeted emotion to express, yet few studies explore how one's emotion is affected by utterances, nor the intentionality of generated sentences @xcite . one exception is emotion elicitation, which considers generating responses that elicit a prespecified emotion in the other party @xcite . though natural for humans to recognize and intentionally influence other's emotions, eliciting pre-specified emotions is challenging for dialogue models @xcite . prior work has evolved from statistical response generator @xcite to neural networks @xcite . all existing models focus on eliciting a single coarse-grained sentiment: positive emotion @xcite . however, as shown in figure 1 , positive sentiment can include more fine-grained emotions such as "hopeful", "joy" and "surprise", which can further serve to deepen the model's understanding of effect, if not intention. by incorporating more emotions in training, it ameliorates the performance in the elicitation of positive emotions. besides, existing work is mostly based on smallscale human-annotated datasets, which limits its capacity of eliciting various emotions. we fill this gap by proposing the first model for emotion elicitation that controls the generation of responses that elicit various pre-specified emotions. due to difficulties in annotation, we represent the elicited emotions using latent variables in order to take full advantage of the large-scale unannotated dataset, choosing conditional variational auto-encoder (cvae) as a backbone @xcite @xcite . two discriminators are further used to control the generation of responses. we reconstruct a recent multi-modal memor dataset @xcite , extracting useful text data for our task, and conduct experiments on nine primary emotions 1 . a large-scale tv show dataset is used to pretrain the model in an unsupervised fashion. results show that our model outperforms the state-of-the-art single-emotion elicitation model @xcite , achieving higher accuracy for eliciting positive emotions. using all emotions in pretraining and finetuning produces the best performance in eliciting positive emotions. in addition, our results show that rich emotion elicitation is a challenging task for current neural models and there is a need for more effective few-shot learning. our code and data will be available at https://github.com/taolusi/eecvae . ## baseline: empdg as shown in figure 2 (a), empdg @xcite is a sequence-to-sequence dialogue response generation model that enhances the elicitation of positive 1 plutchik (1980)'s 9 primary emotions: joy, anger, disgust, sadness, surprise, fear, anticipation, trust and neutral. emotion through empathy. during encoding, the dialogue context is represented as a vector c; during decoding, the generator uses two cnn discriminators to generate an n-token response x. specifically, a semantic discriminator d sem measures the distance from the generated response to the gold response, while an emotional discriminator d emo specifies the degree of empathy in responses. both discriminators are used to extend a transformer model @xcite , serving as semantic and emotional enhancements, respectively. for training, the loss function is defined as follows: ## model the overall structure of our model is shown in figure 2(b ). it can be seen as an extension of cvae (in yellow) with a latent variable and two discriminators to elicit multiple emotions. ## dataset we reconstruct the multi-modal memor dataset @xcite to fit our task and conducted human evaluations to validate the usability in a single modality. memor contains video, audio, and text information of clips from the tv show the big bang theory, with emotion labels given on each character in every clip. we use only the textual data and consider non-speakers' emotions to be the elicited emotions by an utterance. manual decision is made on whether a target emotion can be elicited using text context only, in order to filter dialogues. our reconstructed dataset has a annotator agreement of 80% accuracy (cohen ′ s κ = 0.491). the reconstructed corpus has 22,732 utterances and we split the data into training (18,943), dev (1,894), and test (1,894). nine emotions are labeled in total in the dataset according to the emotion classification of @xcite , out of which 3 positive emotions are chosen as the model output foot_0 . ## conclusion we provided the first discussion on rich emotion elicitation in open-domain dialogue generation, incorporating various positive emotions with a framework that extends cvae with a latent emotion variable equipped with two discriminators. results show that rich emotion elicitation is a challenging task and our model gives more reliable utterances compared with a state-of-art model for single emotion elicitation, and introducing negative emotions in pretraining benefits the model's ability to elicit positive emotions.