Predicting human-targeted translation edit rate via untrained human annotators

NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference

Volumn , Issue , 2010, Pages 369-372

  Zaidan, Omar F ^a   Callison Burch, Chris ^a  

^a Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC METRICS; HUMAN JUDGMENTS; INCREMENTAL CHANGES; MACHINE TRANSLATIONS; MECHANICAL TURKS; ON-LINE SERVICE; SYSTEM DEVELOPMENT;

COMPUTATIONAL LINGUISTICS;

TRANSLATION (LANGUAGES);

EID: 84858436162     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. Chris Callison-Burch. 2009. Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk. In Proceedings of EMNLP.
2. LDC. 2005. Linguistic data annotation specification: Assessment of fluency and adequacy in translations. Revision 1.5.
3. Matthew Snover, Bonnie J. Dorr, Richard Schwartz. 2006. A Study of Translation Edit Rate with Targeted Human Annotation. Proceedings of AMTA.
4. Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008. Cheap and fast - but is it good? Evaluating non-expert annotations for natural language tasks. In Proceedings of EMNLP.