Fact-Aware Summarization with Contrastive Learning for Few-Shot Dialogue State Tracking

Abstract

Dialogue state tracking (DST) is a crucial component of task-oriented dialogue systems, as it aims to accurately track the user’s goals throughout the dialogue history. However, DST models struggle with new domains due to limited annotated data, leading to poor performance. To solve this key challenge in DST, we propose a model called Fact-aware Summarization model for few-shot DST (FaS-DST), which introduces a "Summarize, Extract, and Select" pattern. Specifically, we decompose DST into three sub-tasks: generating candidate summaries, extracting dialogue states, and scoring the candidates to select the most accurate one. Contrastive learning is incorporated to train a candidate scorer, which improves faithfulness and factuality in dialogue summarization. Additionally, we employ two strategies namely data augmentation and summary & state concatenation to improve the model’s training effectiveness. Experimental results demonstrate that FaS-DST outperforms state-of-the-art models on both MultiWOZ 2.0 and MultiWOZ 2.1 datasets in few-shot settings.