  We present our work on Track 4 in the Dialogue System Technology Challenges 8
(DSTC8). The DSTC8-Track 4 aims to perform dialogue state tracking (DST) under
the zero-shot settings, in which the model needs to generalize on unseen
service APIs given a schema definition of these target APIs. Serving as the
core for many virtual assistants such as Siri, Alexa, and Google Assistant, the
DST keeps track of the user's goal and what happened in the dialogue history,
mainly including intent prediction, slot filling, and user state tracking,
which tests models' ability of natural language understanding. Recently, the
pretrained language models have achieved state-of-the-art results and shown
impressive generalization ability on various NLP tasks, which provide a
promising way to perform zero-shot learning for language understanding. Based
on this, we propose a schema-guided paradigm for zero-shot dialogue state
tracking (SGP-DST) by fine-tuning BERT, one of the most popular pretrained
language models. The SGP-DST system contains four modules for intent
prediction, slot prediction, slot transfer prediction, and user state
summarizing respectively. According to the official evaluation results, our
SGP-DST (team12) ranked 3rd on the joint goal accuracy (primary evaluation
metric for ranking submissions) and 1st on the requsted slots F1 among 25
participant teams.
