Parsing Coordination for Spoken Language Understanding

TL;DR

This paper introduces a domain-agnostic shallow parser for spoken language understanding systems that effectively handles coordination structures, improving the parsing of compound entities and intents across domains.

Contribution

The work presents a novel coordination parser that learns domain-independent features and employs adversarial training to enhance generalization across slot types.

Findings

The parser successfully segments conjunct boundaries across various phrasal categories.

Adversarial training improves generalization for coordination parsing across slot types.

The model is domain-agnostic and learns slot-independent features.

Abstract

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.