Name
Abstract | ||
|---|---|---|
Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform. In this paper, we design simple truth discovery methods inspired by emph{proxy voting}, that give higher weight to workers whose answers are close to those of other workers. prove that under standard statistical assumptions, proxy-based truth discovery (PTD) allows us to estimate the true competence of each worker, whether workers face questions whose answers are real-valued, categorical, or rankings. We then demonstrate through extensive empirical study on synthetic and real data that PTD is substantially better than unweighted aggregation, and competes well with other truth discovery methods, in all of the above domains. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Artificial Intelligence | Journal |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reshef Meir | 1 | 344 | 33.65 |
| Ofra Amir | 2 | 67 | 9.32 |
| Gal Cohensius | 3 | 0 | 0.34 |
| Omer Ben-Porat | 4 | 3 | 6.54 |
| Lirong Xia | 5 | 1034 | 86.84 |