Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Hongxuan Tang"'
Autor:
Lanze Li, Qinghe Cao, Yitian Wu, Yu Zheng, Hongxuan Tang, Jiujiu Ge, Mengdi Liang, Bao Zhou, Baiyu Jiang, Sai Wu, Fan Wang, Yajun Pang, Zhehong Shen, Cao Guan, Hao Chen
Publikováno v:
Advanced Materials.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e3de533f3588595557b3990dc09e191
https://doi.org/10.1002/adma.202300132
https://doi.org/10.1002/adma.202300132
Autor:
Hongxuan Tang, Jiujiu Ge, Lanze Li, Xinqiang Zhu, Sai Wu, Fan Wang, Yajun Pang, Zhehong Shen, Cao Guan, Hao Chen
Publikováno v:
Chinese Chemical Letters. 34:107768
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22a334b974143c9eee6cbd412365784c
https://doi.org/10.1016/j.cclet.2022.107768
https://doi.org/10.1016/j.cclet.2022.107768
Publikováno v:
ACL/IJCNLP (2)
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::adc1e4b4a7b39f9821c754b9316b42b9
https://doi.org/10.18653/v1/2021.acl-short.120
https://doi.org/10.18653/v1/2021.acl-short.120
Autor:
Yajun Pang, Lanze Li, Yanan Wang, Xinqiang Zhu, Jiujiu Ge, Hongxuan Tang, Yu Zheng, Fan Wang, Sai Wu, Qiang Wu, Zhehong Shen, Hao Chen
Publikováno v:
Chemical Engineering Journal. 436:135202
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7282b82de875d9870e37886a019c0de
https://doi.org/10.1016/j.cej.2022.135202
https://doi.org/10.1016/j.cej.2022.135202
Publikováno v:
IALP
“Which city has the larger population, Tokyo or New York?”. To answer the question, in general, we necessarily obtain the prior knowledge about the populations of both cities, and accordingly determine the answer by numeric comparison. Using Mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6246df9df434eccf6b79f29193f1daf
https://doi.org/10.1109/ialp48816.2019.9037729
https://doi.org/10.1109/ialp48816.2019.9037729
Publikováno v:
IALP
Question Generation (QG) aims to automati-nerate questions by understanding the semantics of source sentences and target answers. Learning to generate diverse questions for one source sentence with different target answers is important for the QG tas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::451f2350c4d53936db93d118804cdad3
https://doi.org/10.1109/ialp48816.2019.9037710
https://doi.org/10.1109/ialp48816.2019.9037710