2014 - Fellow of Alfred P. Sloan Foundation
His primary areas of study are Natural language processing, Artificial intelligence, Machine translation, Translation and Sentence. Chris Callison-Burch works on Natural language processing which deals in particular with Paraphrase. Artificial intelligence is represented through his Semantic similarity, Speech translation, Ranking, Language model and Computer-assisted translation research.
His Speech translation research is multidisciplinary, incorporating perspectives in Pivot language, Postediting, Interactive machine translation, Hybrid machine translation and Synchronous context-free grammar. His Machine translation study integrates concerns from other disciplines, such as Ranking and Word error rate. His research in Translation intersects with topics in Direct method and Data science.
Chris Callison-Burch focuses on Artificial intelligence, Natural language processing, Machine translation, Translation and Speech recognition. He interconnects Crowdsourcing and Machine learning in the investigation of issues within Artificial intelligence. In Natural language processing, Chris Callison-Burch works on issues like Grammar, which are connected to Synchronous context-free grammar.
His studies deal with areas such as Computational linguistics and Parsing as well as Machine translation. His studies in Sentence integrate themes in fields like Information retrieval and Fluency. His Language model research incorporates themes from Decoding methods, Discriminative model and Computer-assisted translation.
Chris Callison-Burch mainly investigates Artificial intelligence, Natural language processing, Machine learning, Word and Language model. The various areas that Chris Callison-Burch examines in his Artificial intelligence study include Analogy and Set. A large part of his Natural language processing studies is devoted to Machine translation.
His Machine translation study combines topics in areas such as Pipeline and Space. His Machine learning study also includes
Chris Callison-Burch mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Sampling and Set. Chris Callison-Burch mostly deals with Language model in his studies of Artificial intelligence. The concepts of his Language model study are interwoven with issues in Hierarchical database model and Cluster analysis.
He has researched Natural language processing in several fields, including Social media, Word, Word meaning and Fluency. The Fluency study combines topics in areas such as Sentence, Content word, Rewriting and Reinforcement learning. His Machine learning study combines topics from a wide range of disciplines, such as Space, Decoding methods, Set and Natural language.
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Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn;Hieu Hoang;Alexandra Birch;Chris Callison-Burch.
meeting of the association for computational linguistics (2007)
Findings of the 2009 Workshop on Statistical Machine Translation
Chris Callison-Burch;Philipp Koehn;Christof Monz;Josh Schroeder.
workshop on statistical machine translation (2009)
Findings of the 2012 Workshop on Statistical Machine Translation
Chris Callison-Burch;Philipp Koehn;Christof Monz;Matt Post.
workshop on statistical machine translation (2012)
Findings of the 2011 Workshop on Statistical Machine Translation
Chris Callison-Burch;Philipp Koehn;Christof Monz;Omar Zaidan.
workshop on statistical machine translation (2011)
Re-evaluating the Role of Bleu in Machine Translation Research
Chris Callison-Burch;Miles Osborne;Philipp Koehn.
conference of the european chapter of the association for computational linguistics (2006)
PPDB: The Paraphrase Database
Juri Ganitkevitch;Benjamin Van Durme;Chris Callison-Burch.
north american chapter of the association for computational linguistics (2013)
Paraphrasing with Bilingual Parallel Corpora
Colin Bannard;Chris Callison-Burch.
meeting of the association for computational linguistics (2005)
Method and apparatus for providing multilingual translation over a network
Christopher Callison-Burch;Jeffrey Chin;Raymond Flournoy;Pria Hidisyan.
Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk
empirical methods in natural language processing (2009)
Findings of the 2013 Workshop on Statistical Machine Translation
Ondřej Bojar;Christian Buck;Chris Callison-Burch;Christian Federmann.
workshop on statistical machine translation (2013)
Profile was last updated on December 6th, 2021.
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