Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text. As a matter of fact, humans classify documents primarily based on the semantic meaning of the subcategories. We propose a novel model structure via siamese BERT and interactive double attentions named IDEA ( Interactive DoublE Attentions) to capture the information exchange of text and label names. Interactive double attentions enable the model to exploit the inter-class and intra-class information from coarse to fine, which involves distinguishing among all labels and matching the semantical subclasses of ground truth labels. Our proposed method outperforms the state-of-the-art methods using label texts significantly with more stable results.

翻译：目前的文本分类方法通常仅仅将文本编码成嵌入一个天真的或复杂的分类器之前,这忽略了标签文本中所包含的建议信息。事实上,人类主要根据亚类的语义含义对文件进行分类。我们建议通过Siamese BERT和互动的双重关注(互动的DoublE attentions)建立一个新型模型结构,以捕捉文本和标签名称的信息交流。互动的双重关注使得模型能够利用从粗糙到细细的类别间和类别内信息,这涉及对所有标签进行区分,并匹配地面真相标签的语义子类别。我们提出的方法在使用标签文本时明显使用更稳定的结果,优于最先进的方法。