Domainless Adaptation by Constrained Decoding on a Schema Lattice

In many applications such as personal digital assistants, there is a constant need for new domains
to increase the system’s coverage of user queries. A conventional approach is to learn a separate
model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck
for scaling to a large number of domains. In this paper, we introduce a framework that allows us
to have a single model that can handle all domains: including unknown domains that may be created
in the future as long as they are covered in the master schema. The key idea is to remove the
need for distinguishing domains by explicitly predicting the schema of queries. Given permitted
schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under
the schema. The proposed model achieves competitive and often superior performance over the
conventional model trained separately per domain.