Parsing Coordination for Spoken Language Understanding
This work addresses the limitation of narrow semantic parses in spoken language understanding for downstream applications, though it appears incremental as it builds on existing systems by adding coordination parsing.
The paper tackles the problem of expanding spoken language understanding systems to handle compound entities and intents by introducing a domain-agnostic shallow parser for linguistic coordination, resulting in a model that learns domain-independent features and uses adversarial training to improve generalization across slot types.
Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology. The parses contain intents and slots that are directly consumed by downstream domain applications. In this work we discuss expanding such systems to handle compound entities and intents by introducing a domain-agnostic shallow parser that handles linguistic coordination. We show that our model for parsing coordination learns domain-independent and slot-independent features and is able to segment conjunct boundaries of many different phrasal categories. We also show that using adversarial training can be effective for improving generalization across different slot types for coordination parsing.