Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking
Abstract: in zero-shot dialogue state tracking (DST) by enhancing training data diversity through synthetic data generation.
Existing DST datasets are severely limited in the number of application domains and slot types they cover due to the high costs of data collection, restricting their adaptability to new domains.
This work addresses this challenge with a novel, fully automatic data generation approach that creates synthetic zero-shot DST datasets.
Distinguished from previous methods, our approach can generate dialogues across a massive range of application domains, complete with silver-standard dialogue state annotations and slot descriptions.
This technique is used to create the D0T dataset for training zero-shot DST models, encompassing an unprecedented 1,000+ domains.
Experiments on the MultiWOZ benchmark show that training models on diverse synthetic data improves Joint Goal Accuracy by 6.7\%, achieving results competitive with models 13.5 times larger than ours.
Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: dialogue state tracking
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data resources
Languages Studied: English
Submission Number: 686
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