Building Multi-domain Dialog State Trackers from Single-domain Dialogs (2023)

Abstract: Existing multi-domain dialog state tracking (DST) models are developed based on multi-domain dialogs, which require significant manual effort to define domain relations and collect data. This process can be challenging and expensive, particularly when numerous domains are involved. In this paper, we propose a divide-and-conquer (DAC) DST paradigm and a multi-domain dialog synthesis framework, which makes building multi-domain DST models from single-domain dialogs possible. The DAC paradigm segments a multi-domain dialog into multiple single-domain dialogs for DST, which makes models generalize better on dialogs involving unseen domain combinations. The multi-domain dialog synthesis framework merges several potentially related single-domain dialogs into one multi-domain dialog and modifies the dialog to simulate domain relations. The synthesized dialogs can help DST models capture the value transfer between domains. Experiments with three representative DST models on two datasets demonstrate the effectiveness of our proposed DAC paradigm and data synthesis framework.

## introduction in recent years, with the emergence of multidomain dialog datasets @xcite , the focus of task-oriented dialog (tod) research has shifted from single-domain dialog to multi-domain dialog. in a multi-domain dialog, there may be relations between domains, often exhibited through the transfer of slots' values as illustrated in to model the domain relations in multi-domain dialogs, previous works either employ a copy mechanism @xcite or encode a pre-defined schema graph that indicates slot relationships using prior knowledge @xcite @xcite . nevertheless, these models are data-driven, relying on adequate multi-domain dialog data. another line of research is conversational query rewriting (coqr), which rewrites a user query to be self-contained and understandable without the dialog context @xcite @xcite . however, these methods also require manually collecting in-domain query rewriting data. when there is no prior knowledge about the data distribution of multi-domain dialog, building dst models faces two main challenges: (1) how to make models generalize well to multi-domain dialogs involving arbitrary domains, (2) how to enable models to capture domain relations. in this work, we propose a divide-and-conquer (dac) dst paradigm and a multi-domain dialog synthesis framework to address the challenges. the dac paradigm segment a multi-domain dialog into single-domain dialogs and perform dst for each domain separately, bridging the data distribution gap between dialogs involving different domains. in dac, models only need to predict the state of the currently active domain according to the dialog history of that domain and the state of other domains, which greatly saves the computation of reading all dialog history and predicting all domains' states. our proposed data synthesis framework combines multiple single-domain dialogs into a multi-domain dialog and simulates value transfer between slots of different domains. the framework includes (1) identifying candidate slot pairs for cross-domain value transfer, (2) concatenating single-domain dialogs based on candidate slot pairs, (3) rewriting utterances to simulate cross-domain value transfer, and (4) filtering problematic rewrites with a value tagger. the synthesized data can be used to train dst models directly or train a coqr model to resolve cross-domain value transfer before dst. experiments on two datasets with three representative dst models show that our proposed dac paradigm and data synthesis framework significantly improve model performance on multidomain dialogs when only single-domain dialogs are available. in summary, our contributions include: 1. we propose a divide-and-conquer paradigm for dst, which improves model efficiency and generalization on multi-domain dialogs. 2. we propose a data synthesis framework that can generate multi-domain dialogs using only singledomain dialogs, which enables dst models to identify domain relations without annotations. 3. experiments show that our proposed methods can substantially boost multi-domain dst models using single-domain dialogs, which reduces the need to collect real multi-domain dialogs. ## method unlike previous works on multi-domain dst, we assume we only have single-domain dialogs with no access to prior knowledge regarding how domains will correlate in a multi-domain dialog. in this setting, dst models face two challenges: (1) how to generalize to dialogs involving multiple domains, (2) how to model the domain dependencies. we propose a divide-and-conquer dst paradigm and a multi-domain dialog synthesis framework to address the two challenges.