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

arXiv: https://arxiv.org/abs/2107.03884 · HTML: https://mlsanity.com/paper/2107.03884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue](https://mlsanity.com/paper/2009.13570/) (2020)
- [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://mlsanity.com/paper/2004.03066/) (2020)
- [Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering](https://mlsanity.com/paper/2101.00391/) (2021)
- [ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://mlsanity.com/paper/2010.10418/) (2020)
- [Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions](https://mlsanity.com/paper/2008.07559/) (2020)
- [Sentences with Gapping: Parsing and Reconstructing Elided Predicates](https://mlsanity.com/paper/1804.06922/) (2018)
- [Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management](https://mlsanity.com/paper/2104.00660/) (2021)

## Built on by (most cited first, 1 of 1)

- [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://mlsanity.com/paper/2112.02721/) (2021)

## Abstract (arXiv)

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.