Rank-Aware Negative Training for Semi-Supervised Text Classification
Ahmed Murtadha Shengfeng Pan Wen Bo Jianlin Su Xinxin Cao Wenze Zhang Yunfeng Liu
Semi-supervised text classification-based paradigms (SSTC) typically employ
the spirit of self-training. The key idea is to train a deep classifier on
limited labeled texts and then iteratively predict the unlabeled texts as their
pseudo-labels for further training. However, the performance is largely
affected by the accuracy of pseudo-labels, which may not be significant in
real-world scenarios. This paper presents a Rank-aware Negative Training (RNT)
framework to address SSTC in learning with noisy label manner. To alleviate the
noisy information, we adapt a reasoning with uncertainty-based approach to rank
the unlabeled texts based on the evidential support received from the labeled
texts. Moreover, we propose the use of negative training to train RNT based on
the concept that ``the input instance does not belong to the complementary
label''. A complementary label is randomly selected from all labels except the
label on-target. Intuitively, the probability of a true label serving as a
complementary label is low and thus provides less noisy information during the
training, resulting in better performance on the test data. Finally, we
evaluate the proposed solution on various text classification benchmark
datasets. Our extensive experiments show that it consistently overcomes the
state-of-the-art alternatives in most scenarios and achieves competitive
performance in the others. The code of RNT is publicly available
at:https://github.com/amurtadha/RNT.