Relevance Is a Guiding Light: Relevance-aware Adaptive Learning for End-to-end Task-oriented Dialogue System (2024)

Abstract: Retrieving accurate domain knowledge and providing helpful information are crucial in developing an effective end-to-end task-oriented dialogue system (E2ETOD). The field has witnessed numerous methods following the retrieve-then-generate paradigm and training their systems on one specific domain. However, existing approaches still suffer from the Distractive Attributes Problem (DAP): struggling to deal with false but similar knowledge (hard negative entities), which is even more intractable when countless pieces of knowledge from different domains are blended in a real-world scenario. To alleviate DAP, we propose the Relevance-aware Adaptive Learning (ReAL) method, a two-stage training framework that eliminates hard negatives step-by-step and aligns retrieval with generation. In the first stage, we introduce a top-k adaptive contrastive loss and utilize the divergence-driven feedback from the frozen generator to pre-train the retriever. In the second stage, we propose using the metric score distribution as an anchor to align retrieval with generation. Thorough experiments on three benchmark datasets demonstrate ReAL’s superiority over existing methods, with extensive analysis validating its strong capabilities of overcoming in- and cross-domain distractions.

## introduction the advent of an intelligent era has seen rapid advancement of task-oriented dialogue systems (tod), demonstrating their potential and flexibility across various practical applications like health consulting, financial services, and home automation. though conventional pipeline tod systems model the task with interrelated modules and have achieved remarkable results, several limitations exist, such as error propagation @xcite , increased complexity @xcite , and maintenance challenges @xcite ## conclusion in this paper, we propose a two-stage relevanceaware adaptive learning (real) framework to eliminate hard negatives step-by-step and align retrieval with the generation. with the first adaptive pre-training stage and the subsequent end-to-end fine-tuning stage, our model effectively mitigates dap. extensive experiments and analysis demonstrate real's superiority.