Zobrazeno 1 - 10
of 13
pro vyhledávání: '"PinZhen Chen"'
Autor:
Shengshun Duan, Xiao Wei, Fangzhi Zhao, Huiying Yang, Ye Wang, Pinzhen Chen, Jianlong Hong, Shengxin Xiang, Minzhou Luo, Qiongfeng Shi, Guozhen Shen, Jun Wu
Publikováno v:
Advanced Science, Vol 10, Iss 31, Pp n/a-n/a (2023)
Abstract As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remai
Externí odkaz:
https://doaj.org/article/12e96075e5e947898861d331d835f2d6
Autor:
Enqi Chen, Wenjing Hou, Hu Wang, Jing Li, Yangjing Lin, He Liu, Mingshan Du, Lian Li, Xianqi Wang, Jing Yang, Rui Yang, Changru Zhou, Pinzhen Chen, Meng Zeng, Qiandong Yao, Wei Chen
Publikováno v:
Frontiers in Endocrinology, Vol 13 (2022)
PurposeThe aim of this study was to assess quantitatively articular cartilage volume, thickness, and T2 value alterations in meniscus tear patients.Materials and methodsThe study included 32 patients with meniscus tears (17 females, 15 males; mean ag
Externí odkaz:
https://doaj.org/article/4c63a74395584ad590ab4675bb582628
Autor:
Shengshun Duan1, Pinzhen Chen1, Yu-an Xiong2, Fangzhi Zhao1, Zhengyin Jing2, Guowei Du2, Xiao Wei1, Shengxin Xiang1, Jianlong Hong1, Qiongfeng Shi1, Yumeng You2 youyumeng@seu.edu.cn, Jun Wu1 wujunseu@seu.edu.cn
Publikováno v:
Science Advances. 11/29/2024, Vol. 10 Issue 48, p1-11. 11p.
Autor:
Zheng Zhao, Pinzhen Chen
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031183140
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::559db479a78cf58573678576d4bc81ef
https://doi.org/10.1007/978-3-031-18315-7_9
https://doi.org/10.1007/978-3-031-18315-7_9
Autor:
Pinzhen Chen, Zheng Zhao
Publikováno v:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a09bb21989d0636f7d05d5e3ca12d015
Autor:
Zhenhua Zhang, Enqi Chen, Xueyuan Zhang, Jing Yang, Xianqi Wang, PinZhen Chen, Meng Zeng, Mingshan Du, Senlin Xu, Zhiqing Yang, Fei Ren, Wei Chen
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1fd571291d402380f9993d720847a017
https://doi.org/10.2139/ssrn.4260135
https://doi.org/10.2139/ssrn.4260135
Publikováno v:
Proceedings of the 5th International Conference on Signal Processing and Information Communications ISBN: 9783031131806
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c42574c8316ae0444bf2f16e16be64f
https://doi.org/10.1007/978-3-031-13181-3_3
https://doi.org/10.1007/978-3-031-13181-3_3
Autor:
Nikolay Bogoychev, Pinzhen Chen
Machine translation systems are vulnerable to domain mismatch, especially in a low-resource scenario. Out-of-domain translations are often of poor quality and prone to hallucinations, due to exposure bias and the decoder acting as a language model. W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9b2a33b34705b4bd15cdf24f759a046
Publikováno v:
Chen, P, Bogoychev, N, Heafield, K & Kirefu, F 2020, Parallel Sentence Mining by Constrained Decoding . in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . pp. 1672–1678, 2020 Annual Conference of the Association for Computational Linguistics, Virtual conference, Washington, United States, 5/07/20 . https://doi.org/10.18653/v1/2020.acl-main.152
ACL
ACL
We present a novel method to extract parallel sentences from two monolingual corpora, using neural machine translation. Our method relies on translating sentences in one corpus, but constraining the decoding by a prefix tree built on the other corpus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::500ab577461549726ee0ceb708cc4a4a
https://www.pure.ed.ac.uk/ws/files/161087114/Parallel_Sentence_CHEN_DOA03042020_VOR_CC_BY.pdf
https://www.pure.ed.ac.uk/ws/files/161087114/Parallel_Sentence_CHEN_DOA03042020_VOR_CC_BY.pdf
Autor:
Sergio Ortiz Rojas, Marek Strelec, Amir Kamran, Pinzhen Chen, Jaume Zaragoza, William Waites, Kenneth Heafield, Marta Bañón, Philipp Koehn, Hieu Hoang, Leopoldo Pla Sempere, Brian Thompson, Dion Wiggins, Elsa Sarrías, Faheem Kirefu, Gema Ramírez-Sánchez, Mikel L. Forcada, Barry Haddow, Miquel Esplà-Gomis
Publikováno v:
Bañón, M, Chen, P, Haddow, B, Heafield, K, Hoang, H, Esplà-Gomis, M, Forcada, M, Kamran, A, Kirefu, F, Koehn, P, Ortiz-Rojas, S, Pla, L, Ramírez-Sánchez, G, Sarrías, E, Strelec, M, Thompson, B, Waites, W, Wiggins, D & Zaragoza, J 2020, ParaCrawl: Web-Scale Acquisition of Parallel Corpora . in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . pp. 4555–4567, 2020 Annual Conference of the Association for Computational Linguistics, Virtual conference, Washington, United States, 5/07/20 . https://doi.org/10.18653/v1/2020.acl-main.417
ACL
ACL
We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9f4c40d019a0f748816f44701b90ba6
https://hdl.handle.net/20.500.11820/aeb1138d-856e-477a-9ea0-f3ee5900cab1
https://hdl.handle.net/20.500.11820/aeb1138d-856e-477a-9ea0-f3ee5900cab1