Transfer learning has been used in opinion analysis to make use of available language resources for other resource scarce languages. However, the cumulative class noise in transfer learning adversely affects performance when more training data is used. In this paper, we propose a novel method in transductive transfer learning to identify noises through the detection of negative transfers. Evaluation on NLP&CC 2013 cross-lingual opinion analysis dataset shows that our approach outperforms the state-of-the-art systems. More significantly, our system shows a monotonic increase trend in performance improvement when more training data are used.
REF reduce negative transfer in the process of transfer learning.
Cross-lingual Opinion Analysis via Negative Transfer Detection
ACL
Computer Science