CANDLE: Decomposing Conditional and Conjunctive Queries for Task-Oriented Dialogue Systems

TL;DR

This paper introduces CANDLE, a dataset and method for decomposing complex conditional and sequential queries in task-oriented dialogue systems into simpler subqueries, improving intent understanding.

Contribution

It provides a new dataset and baseline taggers for decomposing complex dialogue queries, addressing limitations of existing intent classifiers.

Findings

CANDLE dataset contains 4282 annotated utterances.

Baseline taggers demonstrate effective decomposition of complex queries.

Improved intent classification for complex dialogue queries.

Abstract

Domain-specific dialogue systems generally determine user intents by relying on sentence level classifiers that mainly focus on single action sentences. Such classifiers are not designed to effectively handle complex queries composed of conditional and sequential clauses that represent multiple actions. We attempt to decompose such queries into smaller single action subqueries that are reasonable for intent classifiers to understand in a dialogue pipeline. We release, CANDLE(Conditional & AND type Expressions), a dataset consisting of 4282 utterances manually tagged with conditional and sequential labels, and demonstrates this decomposition by training two baseline taggers.