DDNAS：用于文本分类的离散化可微神经架构搜索
DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification
AI总结：
针对文本NAS缺乏操作融合与层次类别建模的问题，提出DDNAS，通过连续松弛、梯度搜索及互信息离散化层提升文本分类性能。
摘要：
神经架构搜索（NAS）在学习文本表示方面已展现出良好能力。然而，现有的基于文本的NAS既没有对神经操作进行可学习融合以优化架构，也没有编码文本输入背后潜在的层次化分类。本文提出了一种新颖的NAS方法——离散化可微神经架构搜索（Discretized Differentiable Neural Architecture Search，DDNAS），用于文本表示学习和分类。借助架构表示的连续松弛，DDNAS能够使用梯度下降来优化搜索。我们还通过互信息最大化提出了一个新颖的离散化层，该层被施加于每个搜索节点，以建模文本表示中潜在的层次化分类。在八个多样化真实数据集上进行的大量实验表明，DDNAS能够持续优于最先进的NAS方法。尽管DDNAS仅依赖卷积、池化和无操作这三种基本操作作为NAS构建块的候选，但其出色性能十分显著，并且可通过添加更多不同操作进行扩展以获得进一步提升。
英文摘要：
Neural Architecture Search (NAS) has shown promising capability in learning text representation. However, existing text-based NAS neither performs a learnable fusion of neural operations to optimize the architecture, nor encodes the latent hierarchical categorization behind text input. This paper presents a novel NAS method, Discretized Differentiable Neural Architecture Search (DDNAS), for text representation learning and classification. With the continuous relaxation of architecture representation, DDNAS can use gradient descent to optimize the search. We also propose a novel discretization layer via mutual information maximization, which is imposed on every search node to model the latent hierarchical categorization in text representation. Extensive experiments conducted on eight diverse real datasets exhibit that DDNAS can consistently outperform the state-of-the-art NAS methods. While DDNAS relies on only three basic operations, i.e., convolution, pooling, and none, to be the candidates of NAS building blocks, its promising performance is noticeable and extensible to obtain further improvement by adding more different operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
