Bidirectional Multi-channel Semantic Interaction Model of Labels and Texts for Text Classification

Yuan Wang, Yubo Zhou, Peng Hu, Maoling Xu, Tingting Zhao, Yarui Chen

Abstract: Text classification, aiming for discovering corresponding relationships between labels and texts, is a pivotal task in Natural Language Processing (NLP). The existing joint text-label models help input texts to establish early global category semantic awareness via label embedding techniques, but they cannot simultaneously capture literal and semantic relationships between texts and labels. It may lead models to ignore obvious clues or semantic relations on different cognitive levels. In this paper, we propose a Bidirectional Multi-channel semantic Interaction model (BMI) to handle both explicit and implicit category semantics in texts for text classification. On the explicit semantic level, BMI designs a word representation similarity match channel for shallow interaction to get rid of semantic mismatch based on assumptions that words have different meanings under the same context. On the implicit semantic level, BMI provides a novel attended attention mechanism over texts and labels for deep interaction to model bidirectional text explanation for labels and label guidance for texts. Furthermore, a gated residual mechanism is employed to obtain core information of labels to improve efficiency. Experiments on benchmark datasets show that BMI achieves competitive results over 15 strong baseline methods, especially in the case of short texts.