mlsanityData to 14 Oct 2026

DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit

arXiv 2207.12551 ↗First posted 25 Jul 2022cs.CLcited by 6

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Abstract (arXiv)

Dialog system developers need high-quality data to train, fine-tune and assess their systems. They often use crowdsourcing for this since it provides large quantities of data from many workers. However, the data may not be of sufficiently good quality. This can be due to the way that the requester presents a task and how they interact with the workers. This paper introduces DialCrowd 2.0 to help requesters obtain higher quality data by, for example, presenting tasks more clearly and facilitating effective communication with workers. DialCrowd 2.0 guides developers in creating improved Human Intelligence Tasks (HITs) and is directly applicable to the workflows used currently by developers and researchers.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 4 of its 30 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

4 of 30 references.

References · 4

  1. 2017ParlAI: A Dialog Research Software Platformcited by 139
  2. 2020ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systemscited by 52
  3. 2021A Survey of NLP-Related Crowdsourcing HITs: what works and what does notcited by 15
  4. 2020MEEP: An Open-Source Platform for Human-Human Dialog Collection and End-to-End Agent Trainingcited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 2.

6 papers.