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TAUS Roundtable Moscow, Machine Translation in Professional Translation Process-Continuous Customization and Measured Productivity, Anton Voronov, ABBYY Language Services

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Although many companies have found MT inevitable, the threshold of its implementation into professional translation process remains high. Customization of MT requires time, budgets and considerable amount of translation memory databases. The choice and integration of different MT systems into professional translation process is also challenging. So what should we aim for when introducing MT to our existing working environment? This presentation focuses on the means of continuous MT customization via TM and glossaries updated in real time. Anton discusses the ways of how to approach MT engines training, touches upon the practices of measuring the productivity of translators and sees what can be done when there is no large TM corpus to customize the MT system.

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TAUS Roundtable Moscow, Machine Translation in Professional Translation Process-Continuous Customization and Measured Productivity, Anton Voronov, ABBYY Language Services

  1. 1. THURSDAY,  22  May  /15:00  –  15:30     Machine  Transla>on  in  Professional  Transla>on   Process:   Con>nuous  Customiza>on  and  Measured  Produc>vity   Anton  Voronov,  ABBYY  Language  Services     TAUS  ROUNDTABLE  2014    22  May/  Moscow  (Russia)  
  2. 2. Challenge  accepted   •  The  amount  of  content  generated  almost  doubles  annually   •  Transla>on  speed  and  number  of  translators  are  more  or  less  constant   Main  tools  to  increase  produc>vity   •  Dic>onaries,  glossaries   •  Transla>on  memory   •  Machine  transla>on   •  Crowdsourcing   Machine  transla>on   •  Increases  transla>on  speed   •  Enables  new  types  of  content  to  be  translated  
  3. 3. Machine  transla>on   Precondi>ons   •  Big  projects,  >ght  deadlines   •  New,  less  visible  types  of  content   •  Well  structured  texts  with  high  repe>>on  rates   •  Combined  processes  with  flexible  quality  requirements  (e.g.   crowdsourcing)     Requirements   •  Considerable  volume  of  transla>on  memory   •  Customiza>on  of  MT  engines   •  Proper  choice  of  an  MT  system   •  Deep  integra>on  of  MT  systems  into  professional  transla>on  process  
  4. 4. The  Process   •  Extract  and  translate  terminology;   •  Use  linguis>c  assets  both  for  MT  customiza>on  and  transla>on;   •  Evaluate  MT  engines  and  select  the  best  one  for  each  par>cular  text;   •  Retrain  the  engine  based  on  process  results  (dynamically);   •  Control  MT  quality  (dynamically);   •  Decide  which  (MT  or  TM)  is  the  be]er  op>on  for  each  par>cular  segment   based  on  metrics  and  terminology  (dynamically);   •  Control  and  improve  overall  quality  (dynamically);   •  Measure  all  the  parameters  both  to  es>mate  your  PE  rate  and  further   tune  the  process;   •  Have  a  pla^orm  which  will  allow  you  to  do  all  this  automa>cally.  
  5. 5. Transla>on  workflow  automa>on   •  Integrated  terminology  management  process   •  Web-­‐based  CAT  tool   •  Integrated  MT  engines   •  Agile  workflow  automa>on   •  Simultaneous  mul>-­‐user  collabora>on  on  the  same  document   •  Automated  logging  of  every  post-­‐edi>ng  ac>on  and  its  >me   •  Built-­‐in  QA   In  our  case  all  this  is  done  in  ABBYY  SmartCAT,  a  unified  cloud  environment   for  professional  transla>on  automa>on.  
  6. 6. Engine  retraining   Terminology   •  Extract   •  Improve   •  Retrain   Reuse  of  translated  and  post-­‐edited  segments   •  Use  your  post-­‐edited  segments  as  soon  as  possible   •  The  content  translated  now  is  more  relevant    
  7. 7. Dynamic  selec>on  of  assets   Dynamic  engine  selec>on   •  Personal  preferences   •  Edi>ng  effort  and  produc>vity  parameters  at  all  stages   Dynamic  MT  /  TM  selec>on   •  Match  percentage   •  Terminology  consistency   •  Personal  preferences  
  8. 8. Quality   Dynamic  selec>on  of  segments  for  quality  control   •  Edi>ng  history   •  Time  spent   •  Automa>c  QA  results   Dynamic  selec>on  of  process  stages   •  Adjust  your  process  based  on  sta>s>cs  and  confidence  
  9. 9. Our  plans   •  Integrated  term  extrac>on  and  sugges>on   •  Adding  more  context  informa>on   •  More  QA  checks   •  Deeper  integra>on  of  more  metrics  into  the  process  

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