72 citations · 130 across the 4 of their papers we have counts for
4 papers
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…
Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Meng Fang, Trevor Cohn
Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource…
Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection
Meng Fang, Trevor Cohn
Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we int…
Transfer Learning across Networks for Collective Classification
Meng Fang, Jie Yin, Xingquan Zhu
This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning re…