{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T15:39:13Z","timestamp":1770046753589,"version":"3.49.0"},"reference-count":7,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,9,15]]},"abstract":"<jats:p>Initial results of neural architecture search (NAS) in natural language processing (NLP) have been achieved, but the search space of most NAS methods is based on the simplest recurrent cell and thus does not consider the modeling of long sequences. The remote information tends to disappear gradually when the input sequence is long, resulting in poor model performance. In this paper, we present an approach based on dual cells to search for a better-performing network architecture. We construct a search space that is more compatible with language modeling tasks by adding an information storage cell inside the search cell, so that we can make better use of the remote information of the sequence and improve the performance of the model. The language model searched by our method achieves better results than those of the baseline method on the Penn Treebank data set and WikiText-2 data set.<\/jats:p>","DOI":"10.3233\/jifs-210207","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T12:48:46Z","timestamp":1620391726000},"page":"3985-3992","source":"Crossref","is-referenced-by-count":2,"title":["Dual-cell differentiable architecture search for language modeling"],"prefix":"10.1177","volume":"41","author":[{"given":"Quan","family":"Wan","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Lin","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210207_ref2","first-page":"770","article-title":"Deep residual learning for image recognition[C]\/\/","volume":"2016","author":"He","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"10.3233\/JIFS-210207_ref5","unstructured":"Sutskever I. , Vinyals O. and Le Q.V. , Sequence to Sequence Learning with Neural Networks[C]\/\/ NIPS. MIT Press (2014)."},{"key":"10.3233\/JIFS-210207_ref12","doi-asserted-by":"crossref","unstructured":"Zoph B. , Vasudevan V. , Shlens J. and Le Q.V. , Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, (2018), 8697\u20138710.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"10.3233\/JIFS-210207_ref21","doi-asserted-by":"crossref","unstructured":"Dong X. and Yang Y. , Searching for a robust neural architecture in four gpu hours, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2019).","DOI":"10.1109\/CVPR.2019.00186"},{"key":"10.3233\/JIFS-210207_ref22","unstructured":"So D.R. , Liang C. and Le Q.V. , The evolved transformer, In Proceedings of the 36th International Conference on Machine Learning (ICML). (2019)."},{"key":"10.3233\/JIFS-210207_ref24","doi-asserted-by":"crossref","unstructured":"Xia Y. , Tan X. , Tian F. , Gao F. , Chen W. , Fan Y. , Gong L. , Leng Y. , Luo R. , Wang Y. , Wu L. , Zhu J. , Qin T. and Liu T.-Y. , Microsoft Research Asia\u2019s Systems for WMT19. In Proceedings of the Fourth Conference on Machine Translation, Florence, Italy, Association for Computational Linguistics (2019).","DOI":"10.18653\/v1\/W19-5348"},{"key":"10.3233\/JIFS-210207_ref25","doi-asserted-by":"crossref","unstructured":"Jiang Y. , Hu C. , Xiao T. , Zhang C. and Zhu J. , Improved differentiable architecture search for language modeling and named entity recognition, In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP- IJCNLP), pages 3583\u20133588, Hong Kong, China. Association for Computational Linguistics. (2019).","DOI":"10.18653\/v1\/D19-1367"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-210207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T04:18:04Z","timestamp":1770005884000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-210207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":7,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-210207","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}