UNCERTAINTY-AWARE PSEUDO-LABELING FOR SPOKEN LANGUAGE ASSESSMENT

Automatic spoken language assessment has gained popularity in computer-assisted language learning (CALL). Normally building these systems relies heavily on labor-intensive human-labeled data. In this paper, we adopt the pseudo-labeling (PL) method to make better use of the massive amount of unlabeled data. Traditional pseudo-labeling mechanism selects predictions of high confidence from the unlabeled pool to augment the labeled data, which may be over-confident and less informative. To select more informative, less noisy data, we optimize the pseudo-labeling process by modeling both data and knowledge uncertainty. Based on a self-adaptive learning mechanism, we sequentially and iteratively select unlabeled samples to augment the training sets during the multi-step self-training procedure until the stop criterion is satisfied. Experimental results based on data from the spoken English tests demonstrate superior performance compared to the baselines and other semi-supervised learning (SSL) methods in Pearson correlation coefficient (PCC) and accuracy.