Natural language understanding is the core of the human computer interactions. However, building new domains and tasks that need a separate set of models is a bottleneck for scaling to a large number of domains and experiences. In this paper, we propose a practical technique that addresses this issue in a web-scale language understanding system: Microsoft's personal digital assistant Cortana. The proposed technique uses a constrained decoding method with a universal slot tagging model sharing the same schema as the collection of slot taggers built for each domain. The proposed approach allows reusing of slots across different domains and tasks while achieving virtually the same performance as those slot taggers trained per domain fashion.
There are also a line of prior works on multi-domain learning to leverage existing domains: REF proposed a constrained decoding method with a universal slot tagging model.
Natural Language Model Re-usability for Scaling to Different Domains
EMNLP
Computer Science