46 citations · 46 across the 2 of their papers we have counts for
3 papers
cs.LG2019
Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations
Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn
Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual work…
cs.CL2019
Massively Multilingual Transfer for NER
Afshin Rahimi, Yuan Li, Trevor Cohn
In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few…
cs.CL2017★ 46 cited
Learning how to Active Learn: A Deep Reinforcement Learning Approach
Meng Fang, Yuan Li, Trevor Cohn
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection m…