论文标题
想法：互动式双重注意文本分类的标签嵌入
IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
论文作者
论文摘要
当前的文本分类方法通常仅在幼稚或复杂的分类器之前将文本编码为嵌入，该分类器忽略了标签文本中包含的建议信息。实际上，人类主要基于子类别的语义含义对文档进行分类。我们通过暹罗伯特（Siamese Bert）和互动式双重注意提出了一种新颖的模型结构，名为IDEA（交互式双重注意），以捕获文本和标签名称的信息交换。交互式双重注意使该模型可以利用从粗糙到细小的阶层和类内的信息来利用类别和级别的信息，这涉及区分所有标签并匹配地面真实标签的语义子类。我们所提出的方法的表现优于最新方法，使用标签文本明显具有更稳定的结果。
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.
