@inproceedings{li-etal-2020-learning,
title = "Learning Architectures from an Extended Search Space for Language Modeling",
author = "Li, Yinqiao and
Hu, Chi and
Zhang, Yuhao and
Xu, Nuo and
Jiang, Yufan and
Xiao, Tong and
Zhu, Jingbo and
Liu, Tongran and
Li, Changliang",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.592",
doi = "10.18653/v1/2020.acl-main.592",
pages = "6629--6639",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Learning Architectures from an Extended Search Space for Language Modeling
%A Li, Yinqiao
%A Hu, Chi
%A Zhang, Yuhao
%A Xu, Nuo
%A Jiang, Yufan
%A Xiao, Tong
%A Zhu, Jingbo
%A Liu, Tongran
%A Li, Changliang
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-learning
%X 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.
%R 10.18653/v1/2020.acl-main.592
%U https://aclanthology.org/2020.acl-main.592
%U https://doi.org/10.18653/v1/2020.acl-main.592
%P 6629-6639
Markdown (Informal)
[Learning Architectures from an Extended Search Space for Language Modeling](https://aclanthology.org/2020.acl-main.592) (Li et al., ACL 2020)
ACL
- Yinqiao Li, Chi Hu, Yuhao Zhang, Nuo Xu, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, and Changliang Li. 2020. Learning Architectures from an Extended Search Space for Language Modeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6629–6639, Online. Association for Computational Linguistics.