XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak
  Supervision
Daniel Hajialigol Hanwen Liu Xuan Wang
Text classification aims to effectively categorize documents into pre-defined
categories. Traditional methods for text classification often rely on large
amounts of manually annotated training data, making the process time-consuming
and labor-intensive. To address this issue, recent studies have focused on
weakly-supervised and extremely weakly-supervised settings, which require
minimal or no human annotation, respectively. In previous methods of weakly
supervised text classification, pseudo-training data is generated by assigning
pseudo-labels to documents based on their alignment (e.g., keyword matching)
with specific classes. However, these methods ignore the importance of
incorporating the explanations of the generated pseudo-labels, or saliency of
individual words, as additional guidance during the text classification
training process. To address this limitation, we propose XAI-CLASS, a novel
explanation-enhanced extremely weakly-supervised text classification method
that incorporates word saliency prediction as an auxiliary task. XAI-CLASS
begins by employing a multi-round question-answering process to generate
pseudo-training data that promotes the mutual enhancement of class labels and
corresponding explanation word generation. This pseudo-training data is then
used to train a multi-task framework that simultaneously learns both text
classification and word saliency prediction. Extensive experiments on several
weakly-supervised text classification datasets show that XAI-CLASS outperforms
other weakly-supervised text classification methods significantly. Moreover,
experiments demonstrate that XAI-CLASS enhances both model performance and
explainability.