# Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots

arXiv: https://arxiv.org/abs/2109.09420 · HTML: https://mlsanity.com/paper/2109.09420/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity](https://mlsanity.com/paper/2008.04935/) (2020)
- [Outlier Detection for Improved Data Quality and Diversity in Dialog Systems](https://mlsanity.com/paper/1904.03122/) (2019)
- [Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection](https://mlsanity.com/paper/1704.05753/) (2017)

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

- [BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models](https://mlsanity.com/paper/2509.24101/) (2025)

## Abstract (arXiv)

A prominent approach to build datasets for training task-oriented bots is crowd-based paraphrasing. Current approaches, however, assume the crowd would naturally provide diverse paraphrases or focus only on lexical diversity. In this WiP we addressed an overlooked aspect of diversity, introducing an approach for guiding the crowdsourcing process towards paraphrases that are syntactically diverse.