  We demonstrate substantial performance gains 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.
