Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
This addresses the challenge of adapting sentiment models to new domains without target data during training, but it is incremental as it builds on existing transfer learning approaches.
The paper tackles the problem of domain-invariant sentiment classification by introducing a two-stage training procedure using weakly supervised datasets, achieving performance close to supervised training on target domains.
The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing approaches focus on learning a common representation by leveraging both source and target data during training. In this paper, we introduce a two-stage training procedure that leverages weakly supervised datasets for developing simple lift-and-shift-based predictive models without being exposed to the target domain during the training phase. Experimental results show that transfer with weak supervision from a source domain to various target domains provides performance very close to that obtained via supervised training on the target domain itself.