Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations

Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn. Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations. In Ling Liu 0001, Ryen W. White, Amin Mantrach, Fabrizio Silvestri, Julian J. McAuley, Ricardo S. Baeza-Yates, Leila Zia, editors, The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. pages 1028-1038, ACM, 2019. [doi]

@inproceedings{LiRC19,
  title = {Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations},
  author = {Yuan Li and Benjamin I. P. Rubinstein and Trevor Cohn},
  year = {2019},
  doi = {10.1145/3308558.3313459},
  url = {https://doi.org/10.1145/3308558.3313459},
  researchr = {https://researchr.org/publication/LiRC19},
  cites = {0},
  citedby = {0},
  pages = {1028-1038},
  booktitle = {The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019},
  editor = {Ling Liu 0001 and Ryen W. White and Amin Mantrach and Fabrizio Silvestri and Julian J. McAuley and Ricardo S. Baeza-Yates and Leila Zia},
  publisher = {ACM},
  isbn = {978-1-4503-6674-8},
}