Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators

No abstract is available for this paper.

• Publication year

  2010

• Venue

  North American Chapter of the Association for Computational Linguistics

• Publication date

  2010-06-02

• Fields of study

  Linguistics, Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk

  2009cited by this paper
• Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks

  2008cited by this paper
• A Study of Translation Edit Rate with Targeted Human Annotation

  2006cited by this paper
• A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION

  2005cited by this paper

• Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach

  2021cites this paper
• Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering

  2021influential citation
• Interactive-predictive neural multimodal systems

  2019cites this paper
• Online Learning for Effort Reduction in Interactive Neural Machine Translation

  2018cites this paper
• Interactive neural machine translation

  2017cites this paper
• Assessing Translation Ability through Vocabulary Ability Assessment

  2016cites this paper
• Human Effort and Machine Learnability in Computer Aided Translation

  2014cites this paper
• Webbasierte linguistische Forschung: Möglichkeiten und Begrenzungen beim Umgang mit Massendaten

  2014cites this paper
• Improved Online Learning and Modeling for Feature-Rich Discriminative Machine Translation

  2013cites this paper
• Bucking the trend: improved evaluation and annotation practices for ESL error detection systems

  2013cites this paper
• Structural Asymmetries in Machine Translation: The case of English-Japanese

  2013cites this paper
• Establishing the reliability of natural language processing evaluation through linear regression modelling

  2013cites this paper
• Crowdsourcing Annotation for Machine Learning in Natural Language Processing Tasks (NON-FINAL VERSION! Proofread version will be uploaded April 30, 2012.)

  2012cites this paper
• How Good is Web Based MT: A Case Study on Paper Title Translation

  2012cites this paper
• They Can Help: Using Crowdsourcing to Improve the Evaluation of Grammatical Error Detection Systems

  2011cites this paper
• They Can Help : Using Crowdsourcing to Improve the Evaluation of Grammatical Error Detection Systems

  2011cites this paper