2 research outputs found
Contextual Slot Carryover for Disparate Schemas
In the slot-filling paradigm, where a user can refer back to slots in the
context during a conversation, the goal of the contextual understanding system
is to resolve the referring expressions to the appropriate slots in the
context. In large-scale multi-domain systems, this presents two challenges -
scaling to a very large and potentially unbounded set of slot values, and
dealing with diverse schemas. We present a neural network architecture that
addresses the slot value scalability challenge by reformulating the contextual
interpretation as a decision to carryover a slot from a set of possible
candidates. To deal with heterogenous schemas, we introduce a simple
data-driven method for trans- forming the candidate slots. Our experiments show
that our approach can scale to multiple domains and provides competitive
results over a strong baseline.Comment: Accepted at Interspeech 201