J
Jonathan May
Researcher at University of Southern California
Publications - 108
Citations - 3465
Jonathan May is an academic researcher from University of Southern California. The author has contributed to research in topics: Machine translation & Language model. The author has an hindex of 26, co-authored 108 publications receiving 2751 citations. Previous affiliations of Jonathan May include Dresden University of Technology & City University of New York.
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Proceedings ArticleDOI
Transfer Learning for Low-Resource Neural Machine Translation
TL;DR: A transfer learning method is presented that significantly improves Bleu scores across a range of low-resource languages by first train a high-resource language pair, then transfer some of the learned parameters to the low- resource pair to initialize and constrain training.
Proceedings Article
Tuning as Ranking
Mark Hopkins,Jonathan May +1 more
TL;DR: Pro's scalability and effectiveness is established by comparing it to MERT and MIRA and parity is demonstrated on both phrase-based and syntax-based systems in a variety of language pairs, using large scale data scenarios.
Proceedings ArticleDOI
Cross-lingual Name Tagging and Linking for 282 Languages
TL;DR: This work develops a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia that is able to identify name mentions, assign a coarse-grained or fine- grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable.
Proceedings ArticleDOI
Experience Grounds Language
Yonatan Bisk,Ari Holtzman,Jesse Thomason,Jacob Andreas,Yoshua Bengio,Joyce Y. Chai,Mirella Lapata,Angeliki Lazaridou,Jonathan May,Aleksandr Nisnevich,Nicolas Pinto,Joseph Turian +11 more
TL;DR: It is posited that the present success of representation learning approaches trained on large text corpora can be deeply enriched from the parallel tradition of research on the contextual and social nature of language.
Journal ArticleDOI
Training tree transducers
TL;DR: This work motivates the use of tree transducers for natural language and addresses the training problem for probabilistic tree- to-tree and tree-to-string transducers.