论文标题
神经文本分类通过共同学习聚类和对齐
Neural Text Classification by Jointly Learning to Cluster and Align
论文作者
论文摘要
分布文本聚类提供了语义上信息丰富的表示形式，并捕获了每个单词和语义聚类质心之间的相关性。我们通过通过潜在变量模型诱导群集中心并与分布单词嵌入，以丰富令牌的表示并衡量令牌和每个可学习的群群之间的相关性，从而将神经文本聚类方法扩展到文本分类任务。所提出的方法共同学习单词聚类的质心和聚类对齐的对齐，实现了在多个基准数据集上的最新状态，并证明了所提出的群集对准机制确实有利于文本分类。值得注意的是，我们的定性分析明显地说明了拟议模型所学的文本表示与我们的直觉相符。
Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids. We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a latent variable model and interacting with distributional word embeddings, to enrich the representation of tokens and measure the relatedness between tokens and each learnable cluster centroid. The proposed method jointly learns word clustering centroids and clustering-token alignments, achieving the state of the art results on multiple benchmark datasets and proving that the proposed cluster-token alignment mechanism is indeed favorable to text classification. Notably, our qualitative analysis has conspicuously illustrated that text representations learned by the proposed model are in accord well with our intuition.
