  Neural architecture search (NAS) has advanced significantly in recent years
but most NAS systems restrict search to learning architectures of a recurrent
or convolutional cell. In this paper, we extend the search space of NAS. In
particular, we present a general approach to learn both intra-cell and
inter-cell architectures (call it ESS). For a better search result, we design a
joint learning method to perform intra-cell and inter-cell NAS simultaneously.
We implement our model in a differentiable architecture search system. For
recurrent neural language modeling, it outperforms a strong baseline
significantly on the PTB and WikiText data, with a new state-of-the-art on PTB.
Moreover, the learned architectures show good transferability to other systems.
E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity
recognition (NER) tasks and CoNLL chunking task, indicating a promising line of
research on large-scale pre-learned architectures.
