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Chris Callison-Burch
Chris Callison-Burch
Professor, University of Pennsylvania
Подтвержден адрес электронной почты в домене upenn.edu - Главная страница
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Процитировано
Процитировано
Год
Moses: Open source toolkit for statistical machine translation
P Koehn, H Hoang, A Birch, C Callison-Burch, M Federico, N Bertoldi, ...
Proceedings of the 45th annual meeting of the association for computational …, 2007
72952007
Re-evaluating the role of BLEU in machine translation research
C Callison-Burch, M Osborne, P Koehn
Proceedings of EACL 2006, 249-256, 2006
10792006
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
9992022
PPDB: The paraphrase database
J Ganitkevitch, B Van Durme, C Callison-Burch
Proceedings of NAACL-HLT, 758-764, 2013
9622013
Findings of the 2009 workshop on statistical machine translation
C Callison-Burch, P Koehn, C Monz, J Schroeder
8422009
Paraphrasing with bilingual parallel corpora
C Bannard, C Callison-Burch
Proceedings of the 43rd Annual Meeting on Association for Computational …, 2005
7942005
A data-driven analysis of workers' earnings on Amazon Mechanical Turk
K Hara, A Adams, K Milland, S Savage, C Callison-Burch, JP Bigham
Proceedings of the 2018 CHI conference on human factors in computing systems …, 2018
7072018
Method and apparatus for providing multilingual translation over a network
J Chin, C Callison-Burch, R Flournoy, P Hidisyan, R Horiuchi, Y Kassum, ...
US Patent App. 09/825,437, 2001
6712001
Optimizing statistical machine translation for text simplification
W Xu, C Napoles, E Pavlick, Q Chen, C Callison-Burch
Transactions of the Association for Computational Linguistics 4, 401-415, 2016
6582016
Fast, cheap, and creative: Evaluating translation quality using Amazon’s Mechanical Turk
C Callison-Burch
Proceedings of the 2009 conference on empirical methods in natural language …, 2009
6552009
Crowdsourcing translation: professional quality from non-professionals
OF Zaidan, C Callison-Burch
Proceedings of ACL (this volume), 2011
5252011
Problems in current text simplification research: New data can help
W Xu, C Callison-Burch, C Napoles
Transactions of the Association for Computational Linguistics 3, 283-297, 2015
5102015
Deduplicating training data makes language models better
K Lee, D Ippolito, A Nystrom, C Zhang, D Eck, C Callison-Burch, N Carlini
arXiv preprint arXiv:2107.06499, 2021
4712021
Edinburgh system description for the 2005 IWSLT speech translation evaluation
P Koehn, A Axelrod, AB Mayne, C Callison-Burch, M Osborne, D Talbot
International Workshop on Spoken Language Translation, 2005
4572005
Creating speech and language data with amazon’s mechanical turk
C Callison-Burch, M Dredze
Proceedings of the NAACL HLT 2010 workshop on creating speech and language …, 2010
4422010
Arabic dialect identification
OF Zaidan, C Callison-Burch
Computational Linguistics 40 (1), 171-202, 2014
4152014
Improved statistical machine translation using paraphrases
C Callison-Burch, P Koehn, M Osborne
Proceedings of the Human Language Technology Conference of the NAACL, Main …, 2006
3992006
PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
E Pavlick, P Rastogi, J Ganitkevitch, B Van Durme, C Callison-Burch
Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015
3922015
Further meta-evaluation of machine translation
C Callison-Burch, C Fordyce, P Koehn, C Monz, J Schroeder
Proceedings of the Third Workshop on Statistical Machine Translation, 70-106, 2008
3162008
Automatic detection of generated text is easiest when humans are fooled
D Ippolito, D Duckworth, C Callison-Burch, D Eck
arXiv preprint arXiv:1911.00650, 2019
3112019
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