S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking
  in the Era of LLMs
Sarkar Snigdha Sarathi Das Chirag Shah Mengting Wan Jennifer Neville Longqi Yang Reid Andersen Georg Buscher Tara Safavi
The traditional Dialogue State Tracking (DST) problem aims to track user
preferences and intents in user-agent conversations. While sufficient for
task-oriented dialogue systems supporting narrow domain applications, the
advent of Large Language Model (LLM)-based chat systems has introduced many
real-world intricacies in open-domain dialogues. These intricacies manifest in
the form of increased complexity in contextual interactions, extended dialogue
sessions encompassing a diverse array of topics, and more frequent contextual
shifts. To handle these intricacies arising from evolving LLM-based chat
systems, we propose joint dialogue segmentation and state tracking per segment
in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a
true open-domain dialogue system, we propose S3-DST, a structured prompting
technique that harnesses Pre-Analytical Recollection, a novel grounding
mechanism we designed for improving long context tracking. To demonstrate the
efficacy of our proposed approach in joint segmentation and state tracking, we
evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as
well as publicly available DST and segmentation datasets. Across all datasets
and settings, S3-DST consistently outperforms the state-of-the-art,
demonstrating its potency and robustness the next generation of LLM-based chat
systems.