Hierarchical Label Generation for Text Classification

Abstract

Hierarchical text classification (HTC) aims to assign the most relevant labels with the hierarchical structure to an input text. However, handling unseen labels with considering a label hierarchy is still an open problem for real-world applications because traditional HTC models employ a pre-defined label set. To deal with this problem, we propose a generation-based classifier that leverages a Seq2Seq framework to capture a label hierarchy and unseen labels explicitly. Because of no available social media datasets that target at HTC, we constructed a new (Blog) dataset using pairs of social media posts and their hierarchical topic labels. Experimental results on the Blog dataset showed the effectiveness of our generation-based classifier over state-of-the-art baseline models. Human evaluation results showed that the quality of generated unseen labels outperforms even the gold labels.

Implicit Sense-labeled Connective Recognition as Text Generation

“…The label of the ISCR task is composed of a discourse relation label and connective words. Considering that it is not difficult to generate connective words with the pre-trained encoder-decoder model, we employ a text generation approach following Li et al (2018) and Kwon et al (2023).…”