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

Domain-specific dialogue systems generally determine user intents by relying on sentence-level classifiers which 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 sub-queries that are reasonable for intent classifiers to understand in a dialogue pipeline. We release CANDLE (Conditional & AND type Expressions), a dataset consisting of 3124 utterances manually tagged with conditional and sequential labels and demonstrates this decomposition by training two baseline taggers.