Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation (2022)

Abstract: Personalized dialogue systems explore the problem of generating responses that are consistent with the user’s personality, which has raised much attention in recent years. Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation. Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user’s personality. Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personalized response generation. In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator. With these multi-level refiners, we can sparsely extract the most valuable information (tokens) from the dialogue history and leverage other similar users’ data to enhance personalization. Experimental results on two real-world datasets demonstrate the superiority of our model in generating more informative and personalized responses.

## introduction recent years have witnessed great progress in building personalized dialogue systems. in general, previous work explores building a personalized dialogue system via two pathways: (1) directly modeling user personality from predefined persona descriptions or user attribute @xcite @xcite ; and (2) implicitly modeling the user personality from the user's dialogue history @xcite . the latter is considered superior as the dialogue history is easy to obtain and comprises rich personalized information. in this paper, we follow the second pathway that automatically learns implicit user profiles from the user's dialogue history to assist in personalized response generation. it is challenging to model user personality directly from the dialogue history. the main reason is that a user's dialogue history may contain massive historical dialogues, which are too heavy to load in the model and likely to be noisy. a straightforward solution is to truncate the dialogue history, as done by existing work @xcite . however, as tremendous information has been wasted, the model's performance is also influenced. on the other hand, we observe that the dialogue history from other users may also be helpful in generating a personalized response for the current user. for example, users with the same interest in "soccer" may talk about similar things on such a topic. this has been overlooked by existing methods. intuitively, the problem of "data explosion" is even more severe in the latter case (when other similar users' dialogue history is also considered). to alleviate these problems, we propose using a hierarchical refiner structure to sparsely extract the most valuable query-aware persona information from both the current and other similar users' dialogue history. by this means, more dialogue history can be utilized for learning user personality and improving response generation. our model is called msp, which stands for modeling and selecting user personality from the dialogue history for generating personalized responses. instead of attending to all dialogue history, msp refines the most valuable historical information that can well portray the user's personality and guide the response generation. specifically, msp consists of three personal information refiners working at different levels and a personalized response generator. at first, a user refiner is designed to select a group of users who have similar interests to the current user. by refining dialogue history at the user level, we can obtain similar data to share information with similar users and avoid mutual interference with other users. then, a topic refiner filters out the current and similar users' dialogue history that has different topics with the current query at the sentence level. next, we design a token refiner to extract the most valuable queryaware user profiles from the remaining dialogue history at the token level. finally, a personalized response generator combines user profiles and the current query to generate responses. given that there is no explicit supervisory signal guiding the refiner to extract an exemplary user profile, we design a supplementary sentence matching task and a joint training method. the generator will construct a pseudo-label to guide the refiner's extraction. our contributions are three-fold: ## related work personalized dialogue generation opendomain dialogue generation has been extensively studied @xcite @xcite @xcite @xcite . recently, personalized dialogue systems have attached more and more attention. typical methods include: (1) explicitly using predefined persona descriptions or attributes as users' profile to generate personalized responses @xcite @xcite ; (2) using user id embeddings to enhance personalized dialogue generation @xcite ; and (3) extracting implicit user profile from users' dialogue history to generate personalized responses @xcite @xcite . since manually collecting user profiles is impractical for large-scale datasets and the user id embeddings perform badly, in this study, we focus on the last group of methods for personalized response generation. dhap @xcite is the state-of-the-art method in personalized dialogue generation. it uses a transformer-based structure to model the user's dialogue history and extract personal information for response generation. unfortunately, this model can only handle a limited number of user dialogue histories, wasting a lot of valuable information. our method has two main differences from dhap: (1) we propose a refiner structure in our model so that more dialogue history can be handled and the most valuable information can be extracted for improving response generation; (2) with our proposed refiner, we can further incorporate more dialogue history from other users (having similar interests) to facilitate personalized dialogue generation. ## methodology in this section, we first formalize our problem and provide an overview of our proposed msp model. then, we describe the details of each component and the model optimization. ## further analysis we further conduct a series of analyses to elaborate our model. all analyses here are based on the result of the weibo dataset, while similar results can be observed on the reddit dataset. ## conclusion in this work, we propose an msp model for personalized response generation. unlike previous related work, we utilize a refiner structure to extract query-aware persona information from large-scale dialogue history. the multi-level refiners can sparsely extract valuable information from dialogue history and leverage similar users' information to enhance the current user's personalization. experimental results confirm the effectiveness of our model in generating informative and personalized responses.