  Given that labeled data is expensive to obtain in real-world scenarios, many
semi-supervised algorithms have explored the task of exploitation of unlabeled
data. Traditional tri-training algorithm and tri-training with disagreement
have shown promise in tasks where labeled data is limited. In this work, we
introduce a new paradigm for tri-training, mimicking the real world
teacher-student learning process. We show that the adaptive teacher-student
thresholds used in the proposed method provide more control over the learning
process with higher label quality. We perform evaluation on SemEval sentiment
analysis task and provide comprehensive comparisons over experimental settings
containing varied labeled versus unlabeled data rates. Experimental results
show that our method outperforms other strong semi-supervised baselines, while
requiring less number of labeled training samples.
