Using Log-linear Models for Tuning Machine Translation Output

No abstract is available for this paper.

• Publication year

  2008

• Venue

  International Conference on Language Resources and Evaluation

• Publication date

  Unknown publication date

• Fields of study

  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.

• MISTRAL: a lattice translation system for IWSLT 2007

  2007cited by this paper
• Moses: Open Source Toolkit for Statistical Machine Translation

  2007cited by this paper
• METIS-II: the German to English MT system

  2007cited by this paper
• Towards hybrid quality-oriented machine translation – on linguistics and probabilities in MT

  2007cited by this paper
• Open Source Toolkit for Statistical Machine Translation: Factored Translation Models and Lattice Decoding

  2006cited by this paper
• Dependency treelet translation: the convergence of statistical and example-based machine-translation?

  2006cited by this paper
• Automating Post-Editing To Improve MT Systems

  2006cited by this paper
• A Practical of Memory-based Approach for Improving Accuracy of MT

  2005cited by this paper
• Example-based Machine Translation Based on TSC and Statistical Generation

  2005cited by this paper
• Minimum Error Rate Training in Statistical Machine Translation

  2003cited by this paper
• Ape: reducing the monkey business in post-editing by automating the task intelligently

  2001cited by this paper
• Automated Postediting of Documents

  1994cited by this paper
• Perspective

  1974cited by this paper

• Seventh Framework Programme Theme 3 Information and Communication Technologies Panacea Project Platform for Automatic, Normalized Annotation and Cost-effective Acquisition of Language Resources for Human Language Technologies Parallel Technology Tools and Resources

  year unknowncites this paper
• METIS-II Low resource machine translation

  year unknowncites this paper