40 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
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations
is a challenging problem. Handling multi-turn interactions requires the system
to understand various conversational use-cases, such as steering, intent
carryover, disfluencies, entity carryover, and repair. The complexity of this
problem is compounded by the fact that these use-cases mix with each other,
often appearing simultaneously in natural language. This work proposes a
non-autoregressive query rewriting architecture that can handle not only the
five aforementioned tasks, but also complex compositions of these use-cases. We
show that our proposed model has competitive single task performance compared
to the baseline approach, and even outperforms a fine-tuned T5 model in
use-case compositions, despite being 15 times smaller in parameters and 25
times faster in latency.Comment: Interspeech 202