Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (2023)

Abstract: Personalizing dialogue agents is important for dialogue systems to generate more specific,consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent without relying on any explicit persona descriptions during inference. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses when conditioning on the predicted profile of the dialogue agent (i.e. “self persona”), and improve the engagingness of the generated responses when conditioning on the predicted persona of the dialogue partner (i.e. “their persona”). We also find that a trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.

## introduction recently, end-to-end dialogue response generation models @xcite @xcite based on recent advances of neural sequence-to-sequence learning models @xcite have gained increasing popularity as they can generate fluent responses. however, as the dialogue agent is trained with datasets containing dialogues from many different speakers, it can not generate personalized responses for the current speaker, making the generated responses less relevant and engaging @xcite . to address this problem, recent studies attempt to personalize dialogue systems by generating dialogue responses conditioning on given persona descriptions have been shown to help dialogue agents perform better @xcite . however, a major drawback of the current dialogue agent personalization approaches is that they require explicit persona descriptions in both training and inference stages, which severely limits their application in real-world scenarios because detailed persona descriptions for current speakers are not available in most scenarios. another problem is that current dialogue personalization approaches are not interpretable and the role of additional persona information is unclear. in this paper, we propose a novel dialogue agent personalization approach that automatically infers the speaker's persona based on the dialogue history which implicitly contains persona information. our model generates personalized dialogue responses based on the dialogue history and the inferred speaker persona, alleviating the necessity of the persona description during inference. specifically, we propose two different approaches to perform persona detection. the first approach learns a "persona approximator" which takes dialogue history as the input and is trained to approximate the output representation of a persona encoder that takes explicit persona description as the input. the second approach instead addresses the persona detection problem as a sequence-tosequence learning problem and learns a "persona generator" which takes the dialogue history as the input and generates the persona description of the speaker. this approach provides a stronger supervision signal compared with the first approach and is more interpretable as the encoded persona information can be decoded to reconstruct the detected persona description. our proposed approach can be used to incorporate both "self-persona" which is the persona information of the dialogue agent, and "theirpersona" which is the persona information of the dialogue partner. on one hand, generating dia- figure 1 : illustration of the proposed persona detection models. the persona approximator is on the left. it is trained to maximize the embedding similarity between persona embedding approximated by the persona approximator and the persona encoder, which is obtained by taking dialogue history and persona description respectively. the persona generator is on the right, which is trained to recover persona description from the dialogue history, thus can also be viewed as a "persona denosing-autoencoder". our experiments on dialogue datasets with and without the persona description demonstrate the effectiveness of the proposed approach and show that a trained persona detection model can be successfully transferred to datasets without persona description. ## related work preliminary study on dialogue personalization @xcite attempts to use a persona-based neural conversation model to capture individual characteristics such as background information and speaking style. however, it requires the current speaker during inference to have sufficient dia-logue utterances included in the training set, which is quite restricted by the cold-start problem. more recently, @xcite released the personachat dataset which incorporates persona of two speakers represented as multiple sentences of profile description to personalize dialogue agents. they propose a profile memory network by considering the dialogue history as input and then performing attention over the persona to be combined with the dialogue history. @xcite proposed to train a persona encoder and combine the encoded persona embedding with context representation by concatenation. the combined representation is then fed into the dialogue decoder to generate personalized responses. @xcite designed the deepcopy model, which leverages copy mechanism to incorporate persona texts @xcite propose to use meta-learning to adapt to the current speaker quickly, their approach also requires several dialogues of the speaker to perform dialogue personalization, which is different from our approach. @xcite propose a dialogue natural language inference dataset and use it to measure and improve the consistency of the dialogue system. more recently, zheng et al. (2019) propose personalized dialogue generation with diversified traits. song et al. (2020) introduce a multi-stage response generation stage to improve the personalization of generated responses. wu et al. (2020) propose a variational response generator to better exploit persona information. different from the aforementioned works, our approach does not require persona information during test time, which makes it more generally applicable. concurrently, ma et al. (2021) propose to infer implict persona base on dialogue histories. ## methodology the motivation behind the proposed approach is that we can learn to detect the profile (i.e., persona) of dialogue speakers based on the dialogue history, which is demonstrated by experimental results in @xcite that we can train a model to effectively distinguish the corresponding persona from randomly sampled negative persona based on the dialogue history. the key idea is to jointly train a persona detection model with a conventional dialogue response generation model. the persona detection model is trained with persona description to infer the persona information based on the dialogue history, which provides persona information for the dialogue model, thus alleviating the necessity of provided persona information during test time. we propose two different persona detection models. the first model is a "persona approximator" and the second is a "persona generator". an overview of the proposed models is illustrated in figure 1 . we describe them in detail in this section, together with a multi-task learning objective which facilitates the training stage of the model. ## conclusion in this paper, we propose a dialogue personalization approach that automatically infers the current speakers' persona based on the dialogue history, which enables neural dialogue systems to generate personalized dialogue responses without using persona description at test time. our experiments on the personachat dataset show that the proposed models can improve the model's consistency and engagingness when conditioning on the inferred persona information of the dialogue agent itself or the dialogue partner. we also conduct experiments on the dailydialog dataset where persona description is not available and find that pre-trained persona detection models can be successfully transferred to other datasets without annotated persona descriptions. this confirms the potential of our approach for dialogue personalization in domains where persona descriptions are not available or expensive to collect. nevertheless, our method still requires annotated persona information during training, which can be hard to get for specific domains. we leave this for future work.