RECAP : Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023)

Abstract: Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model’s performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.

## introduction as tremendous successes have been achieved on open-domain dialogue generation @xcite @xcite , personalized dialogue models have started to draw attention because of their ability to generate consistent and engaging conversations and their potential time-saving utility in on-message predictive generation @xcite @xcite . to generate persona-consistent responses, these models condition on not only dialogue context but user personas, which can be either explicitly given or implicitly learned from the user conversations. early works mostly focus on modeling explicit personas @xcite @xcite . these methods rely on dialogue data paired with user traits, profiles or persona description sentences, which are difficult to collect in practice. moreover, explicit personas usually only contain a few user traits (e.g. age, gender, and location) or a few profile sentences, so the amount of information carried with them is limited, which restricts the models' capability to capture and then express more nuanced personalization. later works develop methods for automatically extracting personas @xcite , in order to help improve content diversity, as compared to that seen in explicit personas. however, these extraction methods still cannot fully use all information from user history conversations. recent works address these issues by incorporating user dialogue history as their implicit profiles @xcite @xcite . these methods generate personalized responses in two phases, retrieving relevant conversations from the user history and fusing the retrieved information to the generator. in the first phase, these methods retrieve a subset of conversations from a user's conversation history @xcite . in the second phase, the retrieved conversations are fused into a decoder by manipulating output logits @xcite or by adding prompt tokens to the input @xcite . even though the implicit profile approach is shown to be the most robust and scalable among all approaches on real-world datasets, it still has some potential weaknesses. approaches to the retrieval phase include using recent conversations @xcite or using conversations based on current context similarity, according to an out-of-domain model @xcite . these approaches have the potential to lose important personal information, which may lead to unexpected behavior and poorly motivated retrieval. in the fusion phase, neither output logit manipulation nor input token prompting may fully leverage the capability of the pre-trained decoder. in this work, we focus on the implicit user profile approach, but specifically address the weaknesses in both retrieval and fusion phases. we present recap, a retrieval-enhanced context-aware prefix encoder for personalized dialogue response generation. similar to other implicit user profile methods, our model is based on a retrieval-fusion approach, which first retrieves persona-relevant information, and then fuses it with conversation context at decode-time. unlike previous work, which does not take the purpose of the retrieval task into consideration, our hierarchical transtormer retriever is trained specifically to retrieve information that will best communicate a user's persona. unlike previous work, which approaches fusion by concatenating retrieved information at the input level @xcite or manipulating logits at the output level @xcite , we adopt a continuous pre-layer prefix approach @xcite , along with a two-step cross-attention projection @xcite , both of which have been shown to be beneficial. these novelties result in a better personalized dialogue model. our main contributions in this work are: ## methodology in this section, we formalize the personalized dialogue response generation task and introduce our proposed recap method. ## results in this section, we discuss the experimental results and further analysis. due to limited time and computational resources, we only report the results from a single run, and run statistical significance tests. 19 ## conclusion in this work, we introduce recap, a personalized dialogue model, which generates responses in a retrieval augmentation manner. unlike retrievers used in previous works, the hierarchical transformer retriever can perform personalized retrieval using user history responses. the context-aware encoder can encode and preserve the most useful information from the retrieved responses and fuse the information to a regular transformer decoder through continuous prefix vectors. extensive experiments confirm that our model is capable of generating fluent, coherent, and personalized responses.