Multi-Arm Active Transfer Learning for Telugu Sentiment Analysis

Transfer learning algorithms can be used when sufficient amount of training data is available in the source domain and limited training data is available in the target domain. The transfer of knowledge from one domain to another requires similarity between two domains. In many resource-poor languages, it is rare to find labeled training data in both the source and target domains. Active learning algorithms, which query more labels from an oracle, can be used effectively in training the source domain when an oracle is available in the source domain but not available in the target domain. Active learning strategies are subjective as they are designed by humans. It can be time consuming to design a strategy and it can vary from one human to other. To tackle all these problems, we design a learning algorithm that connects transfer learning and active learning with the well-known multi-armed bandit problem by querying the most valuable information from the source domain. The advantage of our method is that we get the best active query selection using active learning with multi arm and distribution matching between two domains in conjunction with transfer learning. The effectiveness of the proposed method is validated by running experiments on three Telugu language domain-specific datasets for sentiment analysis.