| Title | A Quality-based Active Sample Selection Strategy for Statistical Machine Translation | 
  
  | Authors | Varvara Logacheva and Lucia Specia | 
  
  | Abstract | This paper presents a new active learning technique for machine translation based on quality estimation of automatically translated sentences. It uses an error-driven strategy, i.e., it assumes that the more errors an automatically translated sentence contains, the more informative it is for the translation system. Our approach is based on a quality estimation technique which involves a wider range of features of the source text, automatic translation, and machine translation system compared to previous work. In addition, we enhance the machine translation system training data with post-edited machine translations of the sentences selected, instead of simulating this using previously created reference translations. We found that re-training systems with additional post-edited data yields higher quality translations regardless of the selection strategy used. We relate this to the fact that post-editions tend to be closer to source sentences as compared to references, making the rule extraction process more reliable. | 
  
  | Topics | Evaluation Methodologies, Statistical and Machine Learning Methods | 
  
  | Full paper  | A Quality-based Active Sample Selection Strategy for Statistical Machine Translation | 
  
  | Bibtex | @InProceedings{LOGACHEVA14.658, author =  {Varvara Logacheva and Lucia Specia},
 title =  {A Quality-based Active Sample Selection Strategy for Statistical Machine Translation},
 booktitle =  {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
 year =  {2014},
 month =  {may},
 date =  {26-31},
 address =  {Reykjavik, Iceland},
 editor =  {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
 publisher =  {European Language Resources Association (ELRA)},
 isbn =  {978-2-9517408-8-4},
 language =  {english}
 }
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