What are the Generator Preferences for End-to-end Task-Oriented Dialog System? (2024)

Abstract: Fully end-to-end task-oriented dialogue (EToD) systems have shown excellent performance, which requires the ability to retrieve entities accurately for generation. Existing methods improve the accuracy of entity retrieval and construct data flows between retrieval results and response generator, achieving promising results. However, most of them suffer from the following issues: (1) The entity is retrieved by directly interacting with the context at a coarse-grained level, so the similarity score may be disturbed by irrelevant attributes; (2) The generator pays equal attention to retrieved entities and the context and does not learn the generation preferences for the current turn. In this paper, we propose a framework called Regulating Preferences of Generator (RPG) based on retrieval results, which includes a generator preference extractor, an entity retriever, and a generator with the gate-controlled preference regulator. The generator preference extractor not only improves the entity retriever by filtering the interference of irrelevant attributes but also provides more focused guidance to the generator by performing inter-turn attribute prediction. Experiments and analyses on three standard benchmarks show that our framework outperforms existing methods and improves the quality of the dialogue.

## introduction task-oriented dialogue systems are designed to help users complete certain specific tasks, such as table booking, hotel booking, ticket booking, and online shopping @xcite . traditional task-oriented dialogue systems include several subtasks, such as dialogue state tracking @xcite , dialogue strategies @xcite , and natural language generation @xcite . ## method our system consists of three key components and implements a two-stage training framework, cf. fig. 2 . we first introduce the problem definition in sec. 2.1, followed by the pre-trained encoder for generator preference extractor in sec. 2.2, and the system training in sec. 2.3, including the generator preference extractor, the entity retriever and the generator with the gate-controlled preference regulator. ## conclusion in this work, we propose an rpg framework that effectively improves the performance of end-to-end task-oriented dialogue systems. rpg consists of two key enhancements. firstly, it reduces the interference of irrelevant attributes on entity retrieval by obtaining fine-grained matching information between contexts and entities and extracting the entity attribute shift matrix as preference signals. secondly, it employs multiple generator preference regulators to process the different types of dialogues. our rpg focuses on the different attention that should be allocated to retrieval results and user input in different situations, providing a more fine-grained guidance for the generator. we hope that this exploration will encourage the community to think about the problems of generator preference.