  Dialogue state tracking (DST) is an essential sub-task for task-oriented
dialogue systems. Recent work has focused on deep neural models for DST.
However, the neural models require a large dataset for training. Furthermore,
applying them to another domain needs a new dataset because the neural models
are generally trained to imitate the given dataset. In this paper, we propose
Schema Encoding for Transferable Dialogue State Tracking (SETDST), which is a
neural DST method for effective transfer to new domains. Transferable DST could
assist developments of dialogue systems even with few dataset on target
domains. We use a schema encoder not just to imitate the dataset but to
comprehend the schema of the dataset. We aim to transfer the model to new
domains by encoding new schemas and using them for DST on multi-domain
settings. As a result, SET-DST improved the joint accuracy by 1.46 points on
MultiWOZ 2.1.
