DDNAS: Discretized Differentiable Neural Architecture Search for Text
  Classification
Kuan-Chun Chen Cheng-Te Li Kuo-Jung Lee
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.