Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Melvin Johnson"'
Autor:
Sarah Browne, Tarweh Carter, Risa Eckes, Greg Grandits, Melvin Johnson, Irene Moore, Laura McNay
Publikováno v:
Contemporary Clinical Trials Communications, Vol 11, Iss , Pp 50-54 (2018)
Introduction: This article describes a retrospective review of participant follow-up and retention strategies in the Partnership for Research on the Ebola Virus in Liberia (PREVAIL) I Vaccine Trial. It illustrates and analyzes strategies used to reta
Externí odkaz:
https://doaj.org/article/b651f10c30d44a5a9b1e3a1012e4f519
Autor:
Alexis Conneau, Ankur Bapna, Yu Zhang, Min Ma, Patrick von Platen, Anton Lozhkov, Colin Cherry, Ye Jia, Clara Rivera, Mihir Kale, Daan van Esch, Vera Axelrod, Simran Khanuja, Jonathan Clark, Orhan Firat, Michael Auli, Sebastian Ruder, Jason Riesa, Melvin Johnson
We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102 langua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d5112276248b7b6fa2427d9ed857587
Publikováno v:
ACL/IJCNLP (2)
Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ecc502f36493adff335cae7874f1a9d
http://arxiv.org/abs/2106.02171
http://arxiv.org/abs/2106.02171
Autor:
Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson
Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the state-of-the-art performance on the XTREME multilingual benchmark by more
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::971d36ebc711e18e4116896239f94bcc
http://arxiv.org/abs/2104.07412
http://arxiv.org/abs/2104.07412
Publikováno v:
ACL/IJCNLP (Findings)
Document-level neural machine translation (DocNMT) achieves coherent translations by incorporating cross-sentence context. However, for most language pairs there's a shortage of parallel documents, although parallel sentences are readily available. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4969ea8b265a7546b3c3dc08a3d513ad
Publikováno v:
NAACL-HLT
Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLMR (Conneau et al., 2020) have proven to be impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages. This
Autor:
White, Melvin Johnson
Publikováno v:
The Mississippi Valley Historical Review, 1918 Jun 01. 5(1), 3-19.
Externí odkaz:
https://www.jstor.org/stable/1886224
Autor:
Greg S. Corrado, Mike Schuster, Fernanda B. Viégas, Zhifeng Chen, Melvin Johnson, Nikhil Thorat, Jeffrey Dean, Quoc V. Le, Macduff Hughes, Martin Wattenberg, Yonghui Wu, Maxim Krikun
Publikováno v:
Transactions of the Association for Computational Linguistics. 5:339-351
We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial to
Publikováno v:
INTERSPEECH
We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation. The network is trained end-to-end, learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15becfa04d691db2c4fd7fdf9e739506