基于双重对齐领域自适应的文本分类模型
首发时间:2021-08-24
摘要:为了有效的进行不同领域之间的迁移学习,考虑了领域间的特征分布差异和标签分布差异,提出基于双重对齐领域自适应的文本分类模型。首先,模型使用相关对其算法(CORAL)对源域与目标域所提出的特征进行特征对齐,将对齐后的特征输入到特征提取器中进一步提取特征;然后输入到分类器和判别器中进行两个领域的特征在正负类别和域类别上达到双重对齐。该方法在亚马逊数据集上进行实验,实验结果证明了模型对的有效性。
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Double Alignment Domain Adaptation Based Text Classification Model
Abstract:In order to perform transfer learning between different domains effectively, we consider the differences in feature distribution and label distribution between both domains, and propose a double alignment domain adaptation text classification model. First, the correlation alignment (CORAL) loss is utilized to align the feature distributions between the source and target domain. The aligned features are input to the feature extractor to further extract features, and then are input to classifier and discriminator, forcing the features of the source and target domains to have clear positive and negative category boundaries and domain category boundaries. Through training, the features of the two domains are double aligned on the positive and negative categories and the domain categories. This model was tested on Amazon review dataset. Experimental results demonstrate the effectiveness of our model.
Keywords: text classification domain adaptation correlation alignment distribution discrepancy
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基于双重对齐领域自适应的文本分类模型
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