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arXiv · 1910.13425

Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification

Pratik Kayal·Mayank Singh·Pawan Goyal
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Abstract

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.

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BibTeXRIS

Pratik Kayal, Mayank Singh, Pawan Goyal. 2019-10-29. Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification. https://doi.org/10.1145/3371158.3371194

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