We report our ongoing work about a new deep architecture working in tandem with a statistical test procedure for jointly training texts and their label descriptions for multi-label and multi-class classification tasks. A statistical hypothesis testing method is used to extract the most informative words for each given class. These words are used as a class description for more label-aware text classification. Intuition is to help the model to concentrate on more informative words rather than more frequent ones. The model leverages the use of label descriptions in addition to the input text to enhance text classification performance. Our method is entirely data-driven, has no dependency on other sources of information than the training data, and is adaptable to different classification problems by providing appropriate training data without major hyper-parameter tuning. We trained and tested our system on several publicly available datasets, where we managed to improve the state-of-the-art on one set with a high margin, and to obtain competitive results on all other ones.

该研究报告介绍了一种新的深度架构，结合统计检验过程共同训练文本及其标签描述，以用于多标签和多类别分类任务，该方法完全基于数据驱动，适用于提供适当的训练数据的不同分类问题，我们在多个公开数据集上进行了测试，其中某个数据集的表现大幅提高，其它数据集的表现也很有竞争力。

将统计假设检验整合进深度神经网络