Zero-Shot Text Classification with Semantically Extended Textual Entailment

Zero-shot text classification (0SHOT-TC) aims to detect classes that the model never seen in the training set, and has attracted much attention in the research community of Natural Language Processing (NLP). The emergence of pretrained language models has fostered the progress of 0SHOT-TC, which turns the task into a textual entailment problem of binary classification. It learns an entailment relatedness (yes/no) between the given sentence (premise) and each category (hypothesis) separately. However, the hypothesis generation paradigms need to be further studied, since the label itself or the label descriptions have limited ability to fully express the category space. Conversely, humans can easily extend a set of words describing the categories to be classified. In this paper, we propose a novel zero-shot text classification method called Semantically Extended Textual Entailment (SETE), which imitates the human's ability in knowledge extension. In the proposed method, three semantic extension methods are used to enrich the categories through a combination of static knowledge (e.g. expert knowledge, knowledge graph) and dynamic knowledge (e.g. language models), and the textual entailment model is finally used for 0SHOT-TC. The experimental results on the benchmarks show that our approach significantly outperforms the current methods in both generalized and nongeneralized 0SHOT-TC.