Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, Zhen Nie
Abstract
High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly annotation interface; (2) training enough annotators efficiently; and (3) reproducibility. To address these problems, we introduce CROWDAQ, an open-source platform that standardizes the data collection pipeline with customizable user-interface components, automated annotator qualification, and saved pipelines in a re-usable format. We show that CROWDAQ simplifies data annotation significantly on a diverse set of data collection use cases and we hope it will be a convenient tool for the community.- Anthology ID:
- 2020.emnlp-demos.17
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- October
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 127–134
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-demos.17
- DOI:
- 10.18653/v1/2020.emnlp-demos.17
- Cite (ACL):
- Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, and Zhen Nie. 2020. Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 127–134, Online. Association for Computational Linguistics.
- Cite (Informal):
- Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ (Ning et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-demos.17.pdf