D
David Chiang
Researcher at University of Notre Dame
Publications - 173
Citations - 7893
David Chiang is an academic researcher from University of Notre Dame. The author has contributed to research in topics: Machine translation & Internal medicine. The author has an hindex of 33, co-authored 132 publications receiving 7482 citations. Previous affiliations of David Chiang include University of Pennsylvania & University of Southern California.
Papers
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Proceedings ArticleDOI
A Hierarchical Phrase-Based Model for Statistical Machine Translation
TL;DR: The model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information, which can be seen as a shift to the formal machinery of syntax-based translation systems without any linguistic commitment.
Journal ArticleDOI
Hierarchical Phrase-Based Translation
TL;DR: A statistical machine translation model that uses hierarchical phrasesphrases that contain subphrasing that is formally a synchronous context-free grammar but is learned from a parallel text without any syntactic annotations is presented.
Posted Content
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig,Chris Dyer,Yoav Goldberg,Austin Matthews,Waleed Ammar,Antonios Anastasopoulos,Miguel Ballesteros,David Chiang,Daniel Clothiaux,Trevor Cohn,Kevin Duh,Manaal Faruqui,Cynthia Gan,Dan Garrette,Yangfeng Ji,Lingpeng Kong,Adhiguna Kuncoro,Gaurav Kumar,Chaitanya Malaviya,Paul Michel,Yusuke Oda,Matthew Richardson,Naomi Saphra,Swabha Swayamdipta,Pengcheng Yin +24 more
TL;DR: DyNet is a toolkit for implementing neural network models based on dynamic declaration of network structure that has an optimized C++ backend and lightweight graph representation and is designed to allow users to implement their models in a way that is idiomatic in their preferred programming language.
Proceedings ArticleDOI
Better k-best Parsing
Liang Huang,David Chiang +1 more
TL;DR: It is shown how the improved output of the efficient algorithms for k-best trees in the framework of hypergraph parsing has the potential to improve results from parse reranking systems and other applications.
Proceedings Article
Forest Rescoring: Faster Decoding with Integrated Language Models
Liang Huang,David Chiang +1 more
TL;DR: This work develops faster approaches for efficient decoding based on k-best parsing algorithms and demonstrates their effectiveness on both phrase-based and syntax-based MT systems.