Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023)

Abstract: Developing an efficient retriever to retrieve knowledge from a large-scale knowledge base (KB) is critical for task-oriented dialogue systems to effectively handle localized and specialized tasks. However, widely used generative models such as T5 and ChatGPT often struggle to differentiate subtle differences among the retrieved KB records when generating responses, resulting in suboptimal quality of generated responses. In this paper, we propose the application of maximal marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. In addition, our approach goes beyond considering solely retrieved entities and incorporates various meta knowledge to guide the generator, thus improving the utilization of knowledge. We evaluate our approach on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models. The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses. The code of this work is available at https://github.com/shenwzh3/MK-TOD.

## introduction task-oriented dialogue systems (tod) assist users to accomplish daily tasks such as restaurants, scheduling appointments, and navigating traffic by leveraging external knowledge bases. among them, pipeline systems @xcite involve several intermediate stages such as dialog state tracking and system policy learning for retrieving knowledge and generating responses. in contrast, end-to-end task-oriented dialog systems (e2e-tod) @xcite have gained increasing concentration for their ability to directly generate responses based on the knowledge base without intermediate annotations. although the end-to-end paradigm appears can i get the address and phone number, please? the address is wark terrace and the phone number is 3363682. ## methodology the framework of our proposed mk-tod is depicted in figure 2 . it consists of a retriever and a response generator. in each dialogue turn, the retriever retrieves a set of relevant entities, which are then combined with retrieval-related meta knowledge and the dialogue context. the generator utilizes this information to generate a response for the current turn. in the following section, we first introduce the notations and provide an overview of our method. then, we delve into the detailed explanations of two crucial components: maximum marginal likelihood and meta knowledge. ## results and analysis in this section, we present the overall results obtained using both large-scale knowledge bases and condensed knowledge bases. besides, we demon-strate the phenomenon of retrieval-generation misalignment and conduct ablation studies. ## conclusion this paper aims to address the retrieval-generation misalignment in end-to-end task-oriented dialogue systems by introducing maximal marginal likelihood to train a perceptive retriever that leverages signals from response generation. to enable the response generator to better distinguish between entities, we explore several methods for incorporating retrieval-related meta knowledge. we also propose to incorporate negative entities to enhance the discriminative capability. experimental results demonstrate that when combined with meta knowledge, the response generator effectively leverages high-quality retrieval knowledge, leading to enhanced quality in the generated responses. through analysis, we observe that previous retrieval-augmented generator models suffer from severe retrieval-generation misalignment, while our method mitigates this misalignment.