TAUS MT SHOWCASE, Strategies for Building Competitive Advantage and Revenue from Machine Translation, Dion Wiggins, Asia Online, 10 April 2013

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This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit.

MosesCore is supported by the European Commission Grant Number 288487 under the 7th Framework Programme.

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TAUS MT SHOWCASE, Strategies for Building Competitive Advantage and Revenue from Machine Translation, Dion Wiggins, Asia Online, 10 April 2013

  1. 1. TAUS  MACHINE  TRANSLATION  SHOWCASE  Strategies for Building CompetitiveAdvantage and Revenue fromMachine Translation14:40 – 15:00Wednesday, 10 April 2013Dion WigginsAsia Online
  2. 2. Business  Strategies  for  Building  Strategic  Advantage  and  Revenue  from   Machine  Transla<on   Dion  Wiggins   Chief  Execu<ve  Officer   dion.wiggins@asiaonline.net    Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  3. 3. Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  4. 4. •  Human  Resources   •  Data  Requirements   –  Linguis@c   –  Third  party   •  Language  /  Transla@on   •  Free,  Commercial   •  Natural  Language   –  Internal  data   Programming  (NLP)   –  Data  manufacturing   –  Technical   –  Clean  vs.  Dirty  Data  SMT   •  Opera@ng  System   –  Rules  vs.  SMT  vs.  Hybrid   •  SoGware  installa@on  and   support   •  Skill  Development   –  Programming   –  Hosted  -­‐  basic  skills   •  Tailoring  to  needs  of  the   –  Onsite  Moses  –   business   comprehensive   •  Integra@on  with  other  tools   and  plaLorms   •  TMS  /  Workflow   •  Infrastructure   Integra@on   –  Hardware   –  Pre-­‐built,  custom   •  Hosted,  purchased   development   –  SoGware   •  Document  Format  Support   •  Licensed,  Hosted,  Open   Source     –  Wide,  limited  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  5. 5. •  Transla@on  Costs   •  Project  Type   –  Monthly  fee,  per  word,   –  Language  Pair   human  resources   –  Domain   •  Customiza@on  Costs   •  Risk   –  Up  front,  embedded  on   –  Managed  by  expert   transla@on  costs,  human   –  Managed  by  your  term   resources   –  Likelihood  of  failure   •  Management  Costs   •  Time  to  Quality   –  Oversight,  improvement     –  Trained  by  professionals,   •  Control   learned  skills   –  Extensive,  limited   •  Cost  of  Post  Edi@ng   •  Data  Security   –  Higher  quality  MT  should   –  Contract,  internal   result  in  lower  cost  of   edi@ng  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  6. 6. M   T   Machine  Transla<on   50  Years  of   eMpTy  Promises   Q   Why  does  an  industry  that  has  spent  50  years   failing  to  deliver  on  its  promises  s@ll  exist?   A   An  infinite  demand  –  a  well  defined  and   growing  problem  that  has  always  been  looking   for  a  solu@on  –  what  was  missing  was  …  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  7. 7. Quality   Control   Focus  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  8. 8. 1.  Customize   2.  Measure   Create  a  new  custom  engine   Measure  the  quality  of  the   using  founda@on  data  and   engine  for  ra@ng  and  future   your  own  language  assets   improvement  comparisons   4.  Manage   3.  Improve   Manage  transla@on  projects   Provide  correc@ve  feedback   while  genera@ng  correc@ve   removing  poten@al  for     data  for  quality  improvement.   transla@on  errors.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  9. 9. Quality requires an understanding of the data There is no exception to this ruleCopyright  ©  2013,  Asia  Online  Pte  Ltd  
  10. 10. 1.  Click  Training  Data  tab.   2.  Click  on  Upload  and  select  TMX  files.   3.  Click  Training  Data  tab.   4.  Click  Build   Some  even  brag  that  it  is  this  simple.       “Seriously,  that’s  it!”   Perhaps  it  should  have  been       “Seriously,  that’s  it????”  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  11. 11. •  Simply  upload  your  data  and   magic  happens  to  create  a   custom  MT  engine  in  hours/ minutes.   •  Seriously,  that’s  it!   Flaws  in  the  One  BuWon  Instant  MT  Approach   •  MT  cannot  not  read  your  mind.   •  It  cannot  determine  which  wri<ng   style,  target  audience,  formats,   vocabulary  or  capitaliza<on  you  want.     •  It  cannot  determine  what  is  missing   and  whether  your  data  is  suitable  for   your  goal.   •  You  don’t  know  which  is  the  right  data  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  12. 12. Just  Add  Water  Upload  Data If  it  was  really  this   easy,  don’t  you  think   custom  MT  success   stories  would  be   everywhere?  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  13. 13. “The  ready  availability  of  the  Moses  MT  engine  under  an  open  source  license   enables  everybody  to  create  staCsCcal  MT  engines  from  parallel  data  with  a   moderate  amount  of  effort.”   •  Moses  Case  study  that  describes  the  effort  in  detail:  hhp://slidesha.re/KwkdUH   •  Summary:   –  Needs  expert  programmer,  expert  project  manager   –  Requires  very  powerful  hardware   –  Large  amounts  of  soGware  development   –  TAUS  Data  Associa@on  membership  EUR  15,000  for  data   –  360  man  hours  to  set  up  first  pilot   –  Mul@-­‐year  effort  with  considerable  funding  required   –  Transla@on  quality  close  to  that  of  Bing     “With  self-­‐serve  MT,  clients  without  the  necessary  MT  and  compuCng  experCse  to   install  Moses  themselves,  have  for  the  first  Cme  the  ability  to  build  an  MT  system   based  on  their  own  user  requirements  preLy  much  instantly.“  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  14. 14. •  Do  it  yourself  Moses  and  Self  Service  Moses  primarily   target  and  solve  the  engineering  complexity  of   deploying  a  basic  Moses  system   •  There  are  many  other  technical  and  data   requirements  necessary   •  Many  addi@onal  technology  components  are  needed.   Some  have  not  yet  been  developed  such  as  TMS   integra@on,  XML  tag  handling  etc.   For  a  good  blog  entry  and  discussion  on  this  topic  see   hLp://bit.ly/rWAxG7  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  15. 15. 1.  What  is  the  right  data  to  upload  for  my  MT  system?   2.  How  should  I  prepare  my  data?   3.  What  cleaning  can  I  do  that  the  magic  1  click  buhon     does  not  do?   4.  What  impact  will  my  data  have  on  the  MT  system?   5.  Will  the  data  I  upload  improve  or  decrease  quality?   6.  What  will  mixing  data  from  mul@ple  domains  do  to  my  MT  system?   7.  Should  I  add  some  or  all  of  the  TAUS  data  to  my  system?   8.  Once  I  have  a  system,  how  can  I  make  it  beher?   9.  When  I  see  an  error  in  my  MT  output,  how  can  I  know  the  cause  of  the  error?   10.  When  I  see  an  error  in  my  MT  output,  how  can  I  fix  the  error?   11.  …   ..   1.  …                                                                                                                              1.          …  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  16. 16. •  Defini@on   –  Domain   –  Target  Audience   –  Preferred  Wri@ng  Style   –  Glossaries,  Non-­‐Translatable  Terms,  Preferred  Capitaliza@on   –  Special  Formapng  Requirements   –  Quality  Requirements   •  Data  Gathering   –  Source  data  in  domain   Provided  by  client  and  gathered   –  Bilingual  data  to  support  domain   from  third  par@es.   –  Monolingual  data  to  support  domain   •  Data  Analysis   –  Gap  analysis   –  High  frequency  terms   –  Term  extrac@on   •  Data  Genera@on   –  Suppor@ng  grammar  structures   –  Source  Data  Analysis   •  Cleaning  of  Data   •  Tuning  and  Test  Set  Prepara@on   •  Diagnos@c  Engine   –  Fine  tuning  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  17. 17. •  Near  human  quality  automated  transla<on  designed   for  the  professional  transla<on  industry   “ We  found  that  52%  of  the  raw   original  output  from  Asia  Online  had   no  errors  at  all  –  which  is  great  for  an   ” –  Many  customers  have  achieved  quality  levels  where  more  than  50%   of  raw  machine  transla@on  requires  no  edi@ng  at  all   ini<al  engine.            .         –  Kevin  Nelson,     –  Case  studies  of  customers  that  have  achieved  3  x  margin  with  1/3  the   human  resources   Managing  Director,     Omnilingua  Worldwide   –  Regularly  replacing  compe@tors  pre-­‐exis@ng  installa@ons   •  Machine  +  Human  approach  delivers  higher  quality   than  a  human  only  approach   –  More  consistent  wri@ng  style  and  more  accurate  terminology   Complete  Stylis<c   •  Rapid  ongoing  transla<on  quality  improvement   –  Post  edited  machine  transla@on  is  fed  back  to  the  engine  which  learns   Control     from  its  previous  errors  by  analyzing  the  correc@ons   Two  different  output  styles   –  Live  feedback  as  new  content  is  published   for  the  same  input  sentence   •  Enable  clients  to  control  preferred  terminology,   vocabulary  and  wri<ng  style  Spanish  Original   Se  necesitó  una  gran  maniobra  polí@ca  muy  prudente  a  fin  de  facilitar  una  Before   cita  de  los  dos  enemigos  históricos.  Transla<on:  Business  News   Significant  amounts  of  cau@ous  poli@cal  maneuvering  were  required  in  order  Aaer  Transla<on:   to  facilitate  a  rendezvous  between  the  two  biher  historical  opponents.  Children’s  Books   A  lot  of  care  was  taken  to  not  upset  others  when  organizing  the  mee@ng  Aaer  Transla<on:P  te  Ltd  Copyright  ©  2013,  Asia  Online   between  the  two  long  @me  enemies.  
  18. 18. LP   Top-­‐Level   Engines/Sub-­‐Domains   Domain   EN-­‐ES   Automo<ve     Honda   Cars   User  Manuals     Engineering  Service  Manuals       Motorbikes   User  Manuals     Engineering  Service  Manuals     Toyota   Marke@ng     Service  Reports  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  19. 19. Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  20. 20. Dirty  Data  SMT  Model   •  Data     from  as  many  sources  as   –  Gathered   possible.   –  Domain  of  knowledge  does  not  maher.   –  Data  quality  is  not  important.     –  Data  quan<ty  is  important.   •  Theory     –  Good  data  will  be  more  sta<s<cally   relevant.     Clean  Data  SMT  Model   •  Data   –  Gathered  from  a  small  number  of   trusted  quality  sources.   –  Domain  of  knowledge  must  match   target   –  Data  quality  is  very  important.   –  Data  quan@ty  is  less  important.   •  Theory   –  Bad  or  undesirable  paAerns  cannot  be   learned  if  they  don’t  exist  in  the  data.    Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  21. 21. Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  22. 22. •  There  is  no  magic  in  MT,  human  effort  is  required.   •  The  quality  of  the  output  and  suitability     for  purpose  is  directly  in  propor@on    to  the  amount  of  human  effort.   •  Without  human  direc@on,     MT  will  cost  more     in  the  long  term     and  is  more  likely     to  fail.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  23. 23. •  Bad  transla@ons  •  Out  of  domain  text  •  Unbalanced  /  Biased   –  Too  much  text  from  other  domains  •  Mixed  /  Wrong  language  •  Junk  and  noise  •  Broken  HTML  •  Mixed  Encoding  •  Missing  diacri@cs     –  café  vs.  cafe  •  OCR  Text  •  Machine  translated  text  •  Anything  that  is  not  high     quality  and  in  domain   Put  Simply:  If  a  bad  paWern  does  not  exist  in   your  training  data,  you  cannot  generate  such  a   bad  paWern  as  transla<on  output.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  24. 24. English  Source   Human  Transla<on   Google  Transla<on   Google  Context   I  went  to  the  bank   Fui  al  banco   Fui  al  banco   Bank  as  in  finance   I  went  to  the  bank  to   Fui  al  banco  para  depositar   Fui  al  banco  a  depositar   Bank  as  in  finance   deposit  money   dinero   el  dinero   I  went  to  the  bank  of   Fui  en  coche  a  la   Fui  a  la  orilla  de  la  vuelta   Bank  as  in  river  bank   the  turn  in  my  car   inclinación  de  la  vuelta   en  mi  coche   I  put  my  car  into  the   Puse  mi  coche  en  la   Pongo  mi  coche  en  el   Bank  as  in  finance   bank  of  the  turn   inclinación  de  la  vuelta.   banco  de  la  vuelta   I  swam  to  the  bank  of   Nadé  en  la  orilla  del  río   Nadé  hasta  la  orilla  del   Bank  as  in  river  bank   the  river   río   I  banked  my  money   Deposité  mi  dinero   Yo  depositado  mi  dinero   Banked  as  in  finance   I  banked  my  car  into  the   Incliné  mi  coche  en  la   Yo  depositado  mi  coche   Banked  as  in  finance   turn   vuelta   en  la  vuelta   I  banked  my  plane  into   Incliné  mi  avión  en  para   Yo  depositado  en  mi   Banked  as  in  finance   a  steep  dive   una  zambullida.   avión  en  picada   Issue:   The  above  examples  show  that  Google  is  biased  towards  the  banking  and  finance  domain   There  is  much  more  mul<lingual  banking  and  finance  data  available  to  learn  from  than   Cause:   there  is  aeronau<cal  or  water  sports  data  available.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  25. 25. •  Compe<tors  require  20%  or   Typical  Dirty  Data  SMT   more  addi<onal  data  than   engines  will  have  between   the  ini<al  training  data  to   2  million  and  20  million   show  notable  improvements.     sentences  in  the  iniCal   –  This  could  take  years  for  most  LSPs   training  data.     –  This  is  the  dirty  lihle  secret  of  the   Dirty  Data  SMT  approach  that  is   frequently  acknowledged.   •  Asia  Online  has  reference   customers  that  have  had   notable  improvements  with   <  0.1%   just  1  days  work  of  post   Improvements   daily  based  on   edi<ng.   edits   –  Only  possible  with  Clean  Data  SMT  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  26. 26. Compe<tors  Sta<s<cal  MT   Language  Studio  Allows:   •  Automated  iden<fica<on  of  areas  of  weakness   •  Post  Edi<ng  Feedback  focusing  directly  on  areas  of   •  Get  more  dirty  data   weakness   •  Human  translate  more  data   •  Automated  error  paWern  analysis  and  correc<on   •  Analysis  and  Resolu<on  of  Unknown  Words   •  Determina<on  and  resolu<on  of  high  frequency   Compe<tors  Rule  Base  MT   phrases   •  Terminology  Extrac<on   •  Balancing  Bilingual  Phrases  against  Monolingual  Data   •  Run<me  glossary   •  Run<me  spelling  dic<onary   •  PaWern  handling  and  adjustments   •  Incremental  Improvement  Training   •  Automated  Quality  Measurement   •  Add  dic<onary  entries  (limit  20K  words)   •  Human  Quality  Measurement     •  Train  a  language  model  to  fix  broken   •  Quality  Confidence  Scores  for  each  segment   rules  output  (limit  40K  phrases)  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  27. 27. •  Typically  about  10-­‐20  examples  for  each   •  Large  volumes  of  dirty  data  prohibits  manual   clean  word  of  phrase.   correc<on.   •  Each  correc<on  has  sta<s<cal  relevance  and   •  Individual  correc<ons  are  not  sta<s<cally   impact  can  be  clearly  seen.   relevant.   •  Correc<ons  usually  involve  adding  data  to   •  Manual  correc<ons  must  compete  against   fill  gaps.   1,000’s  of  bad  examples.  Imprac<cal  to  create   •  Far  less  correc<on  of  actual  errors.   enough  examples  manually.   •  Clean  data  means  cause  of  errors  can  be   •  Understanding  the  cause  of  errors  is  difficult.   understood  and  corrected.   •  Slows  training  and  overall  processing  <me.   •  Concordance  used  to  create  unbiased   Requires  more  resources  to  process  excess   examples/phrases  and  ensure  scope   data.   covered.     •  Only  solu<on  is  to  acquire  more  dirty  data   and  hope  problem  is  fixed.  But  may  get  worse   or  cause  new  errors.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  28. 28. 1960’s   1980’s   1990’s   2012  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  29. 29. Before  Machine  TranslaCon   Source  text  is  processed  and  modified.   Pre-­‐Transla<on  JavaScript  (JS)   -­‐  Complex  pre-­‐processing  can        be  customized  via  JavaScript.   Pre-­‐Transla<on  Correc<ons  (PTC)   -­‐  A  list  of  terms  that  adjust  the  source     AUer  Machine  TranslaCon      text  fixing  common  issues  and     Target  text  is  processed  and  modified.      making  it  more  suitable  for  transla@on.   Non-­‐Translatable  Terms  (NTT)   Post  Transla<on  Adjustment  (PTA)   -­‐  A  list  of  monolingual  terms  that  are      -­‐  A  list  of  terms  in  the  target  language  that        used  to  ensure  key  terms  are  not          modify  the  translated  output.  This  is  very        translated.        useful  for  normaliza@on  of  target  terms.   Run<me  Glossary  (GLO)   Post  Transla<on  JavaScript  (JS)   -­‐  A  list  of  bilingual  terms  that  are  used  to      -­‐  Complex  post-­‐processing  can        ensure  terminology  is  translated  a            be  customized  via  JavaScript.      specific  way.   Run<me  customiza<ons  can  be  applied  in  2  forms: Default:  Applied  to  all  jobs.   Job  Specific:  A  different  set  of  customiza@ons  can  be  applied  for   different  clients.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  30. 30. 12,000  15,000   9,000   18,000   Typical  MT  +     6,000   Post  Edi<ng   21,000   Speed   *   3,000   25,000   Human  T ransla<o n   0   Words  Per  Day  Per  Translator   28,000   Average  person  reads  200-­‐250  words  per  minute.  96,000-­‐120,000  in  8  hours.    ~35  Cmes  faster  than  human  translaCon.  Copyright  ©  2013,  Asia  Online  Pte  Ltd   *Fastest  MT  +  Post  Edi@ng  Speed  reported  by  clients.  
  31. 31. Metrics  That  Really  Count     ProducCvity  is  the    •  Produc<vity  –  Words  per  day  per  human  resource   Best  Quality  Metric  •  Margin  –  2-­‐3  <mes  the  profit  margin  is  commonplace   Raw   MT   oaen   has   a   greater   number   of   errors   than   first  pass  human  transla<on.    •  Consistency  –  Wri<ng  style  and  terminology   However:   ü  MT  +  Human  delivers  higher  quality  than  a  human  only   Language  Studio™  MT  is  stylised  to  a  specific  domain,   approach   customer   and   target   audience,   so   quality   is  •  Deals   considerably  higher  than  other  MT  systems.     ü  New  deals  not  accessible  with  a  human  only  approach   This  means  that:   ü  Deals  where  you  could  offer  a  more  compe@@ve  bid  due   1.  MT  errors  are  easy  to  see  and  easy  to  fix     (i.e.  simple  grammar).     to  MT  than  your  compe@tors   2.  MT  provides  more  accurate  and  consistent   ü  Deals  that  would  have  been  lost  to  a  compe@tor  without   terminology  than  human  translators,  especially   when  more  than  1  human  works  on  a  project.   the  advantages  that  MT  offers   3.  Human  errors  may  be  fewer,  but  harder  to  see   and  harder  to  fix.  Examples  of  other  “Useful”  Quality  Indicators   Coun@ng  the  number  of  errors  only,  offers  no  value  as  Automated  Metrics  (Good  indicators,  but  not  absolute)   a   metric   as   the   complexity   of   the   error   is   not   taken  •  BLEU  (Bilingual  Evalua@on  Understudy)   into  account.    •  NIST   MT  with  more  errors  is  oaen  faster  to  edit  and  fix   than  first  pass  human  transla<ons  with  fewer  errors.    •  F-­‐Measure  (F1  Score  or  F-­‐Score)  •  METEOR  (Metric  for  Evalua@on  of  Transla@on  with  Explicit  ORdering)     Margin  Manual  Quality  Metrics  (Most  not  designed  for  MT,  more  for  HT)   Time  •  Edit  Distance  (Does  not  take  into  account  complexity  of  edit)  •  SAE-­‐J2450  (Industry  specific)  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  32. 32. Wait  for  a  Project     Create  a  Product     That  Requires  MT   For  Resale   •  Opportunis@c  approach   •  Proac@ve  approach   •  Many  LSPs  are  interested  in  MT,  but  not   •  Leverages  exis@ng  transla@on  assets   willing  to  take  the  plunge  without  a  paying   •  Can  be  sold  to  many  clients   client.     •  Easier  to  sell  -­‐  test  and  show   •  Limited  to  one  client   •  Can  sell  mul@ple  language  pairs  at  the  same   @me   •  Harder  to  sell  –  longer  sales  cycle   •  Generally  a  higher  Return  On  Investment   •  OGen  build  one  language  pair    to  try,  before   (ROI)   commipng  to  others   Revenue   Revenue   Recurring  revenues  from  words  translated   Recurring  revenues  from  words  translated   One  @me  revenues  from  resale  of  customiza@on   Preparing  source  data   Post  edi@ng   Post  edi@ng   Preparing  source  data   Run@me  glossary  prepara@on   Terminology  defini@on   Non-­‐translatable  terms  defini@on   Non-­‐Translatable  terms   Unknown  and  high-­‐frequency  phrase  resolu@on  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  33. 33. Faster  Transla@on     Reduce  Project  Costs     Delivery     •  Helps  to  manage  margin  squeeze:   •  New  projects  that  could  not  have  been   –  compete  with  compe@tors  using  cheaper  (perhaps   delivered  on  due  to  @me  and  resource   lower  quality)  resources  or  compe@tors  using  MT   constraints   •  Helps  to  cost  jus@fy  business  cases  that  may   •  Helps  clients  that  want  to  simultaneously   not  be  viable  using  a  human  only  approach   ship  product  in  mul@ple  languages   •  Can  be  used  behind  the  scenes  (like  a   •  New  clients  in  research,  analysis,  data   transla@on  memory)  or  disclosed  to  client   mining  and  discovery  markets   •  More  client  work  in  other  areas  as  a  result  of   •  New  clients  that  need  real-­‐@me  or  near  real-­‐ leG  over  transla@on  budget.   @me  transla@on   Revenue   Revenue   Depending  on  project  or  product  model,   Preparing  source  data   revenues  will  vary.  See  previous  slide.   Run@me  glossary  prepara@on     Non-­‐translatable  terms  defini@on   Addi@onal  revenues  from  client  gepng  more   Post  edi@ng   ROI  and  willing  to  invest  in  new  languages.   Recurring  revenues  from  words  translated  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  34. 34. Expand  Exis@ng   Added      Rela@onships   Func@onality   •  Opportuni@es  to  translate  addi@onal   •  Expand  service  offerings  with  new   material  for  markets  that  may  not  have   features  such  as  mul@lingual  customer   been  cost  viable  with  a  human  only   support   approach   •  Integrate  machine  transla@on  into   •  Reuse  custom  MT  for  mul@ple  purposes   exis@ng  client  technologies,  products   and  services   •  Enable  clients  to  beher  compete  in   markets  that  were  only  par@ally   addressed  due  to  cost  and  @me   Revenue   Revenue   Preparing  source  data   Same  as  for  Expanding  Exis@ng  Rela@ons   Terminology  defini@on     Non-­‐Translatable  terms   Addi@onally  able  to  charge  various  service  fees   Unknown  and  high-­‐frequency  phrase  resolu@on   rela@ng  to  the  new  services  offered.  For   Post  edi@ng   example,  transla@ng  common  Q&A  for   Recurring  revenues  from  words  translated   customer  support  and  a  commission  on   One  @me  revenues  from  resale  of  customiza@on   integrated  mul@lingual  support  products.  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  35. 35. Post  Edi<ng  Cost   6   Cost  Per  Word                MT  learns  from  post  edi@ng  feedback  and  quality  of   5   Post  Edi<ng  (Human  Transla<on)   transla@on  constantly  improves.   4    Cost  of  post  edi@ng  progressively  reduces  as  MT  quality   3   increases  aGer  each  engine  learning  itera@on.   2   1   MT  Post  Edi<ng   1   2   3   4   5   6   Engine  Learning  Itera<on   Post  Edi<ng  Effort  Reduces  Over  Time   Publica<on  Quality  Target    The  post  edi@ng  and  cleanup  effort  gets  easier  as  the   MT  engine  improves.   Quality   Post  Edi<ng    Effort    Ini@al  efforts  should  focus  on  error  analysis  and   correc@on  of  a  representa@ve  sample  data  set.     Raw  MT  Quality    Each  successive  project  should  get  easier  and  more   efficient.   1   2   3   4   5   6   Engine  Learning  Itera<on  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  36. 36. How  Omnilingua  Measures  Quality   –  Triangulate  to  find  the  data   –  Raw  MT  J2450  v.  Historical  Human  Quality  J2450   –  Time  Study  Measurements   –  OmniMT  EffortScore™   Everything  must  be  measured  by  effort  first   –  All  other  metrics  support  effort  metrics   –  Produc@vity  is  key   ∆  Effort  >  MT  System  Cost  +  Value  Chain  Sharing  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  37. 37. •  Built  as  a  Human  Assessment  System:     –  Provides  7  defined  and  ac@onable  error  classifica@ons.   –  2  severity  levels  to  iden@fy  severe  and  minor  errors.     •  Provides  a  Measurement  Score  Between  1  and  0:     –  A  lower  score  indicates  fewer  errors.   –  Objec@ve  is  to  achieve  a  score  as  close  to  0  (no  errors/issues)  as   possible.     •  Provides  Scores  at  Mul@ple  Levels:     –  Composite  scores  across  an  en@re  set  of  data.   –  Scores  for  logical  units  such  as  sentences  and  paragraphs.    Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  38. 38. Asia  Online  v.   Compe<ng  MT  System   Factor   Total  Raw  J2450  Errors   2x  Fewer   Raw  J2450  Score   2x  Beher   Total  PE  J2450  Errors   5.3x  Fewer   PE  J2450  Score   4.8x  Beher   PE  Rate   32%  Faster   “      There  were  far  fewer  errors  produced  by  the  Language  Studio™  custom  MT  engine   than  the  compe<tors  legacy  MT  engine.       “        We  found  that  52%  of  the  raw  original  output   from  Asia  Online  had  no  errors  at  all     ” Notably  there  were  fewer  wrong  meanings,  structural  errors  and  wrong  terms  in  the   –  which  is  great  for  an  ini<al  engine.       Language  Studio™  custom  MT  engine,  that  were  "typical  SMT  problems"  in  the   ” compe@tors  legacy  MT  engine.     “ The  final  transla<on  quality  aaer  post-­‐edi<ng  was  beWer  with  the  new  Language   Studio™  custom  MT  engine  than  the  compe<tors  legacy  MT  engine  and  also  beWer   than  a  human  only  transla<on  approach.     –  Kevin  Nelson,     Managing  Director,     Terminology  was  more  consistent  with  a  combined  Language  Studio™  custom  MT  engine   plus  human  post  edi@ng  approach.     Omnilingua  Worldwide  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  39. 39. •  LSP:  Sajan   •  End  Client  Profile:   –  Large  global  mul@na@onal  corpora@on  in  the  IT  domain.   –  Has  developed  its  own  proprietary  MT  system  that  has  been  developed  over  many  years.   •  Project  Goals   –  Eliminate  the  need  for  full  transla@on  and  limit  it  to  MT  +  Post-­‐edi@ng   •  Language  Pair:     –  English  -­‐>  Simplified  Chinese.   –  English  -­‐>  European  Spanish.   –  English  -­‐>  European  French.   •  Domain:  IT   •  2nd  Itera@on  of  Customized  Engine   –  Customized  ini@al  engine,  followed  by  an  incremental  improvement  based  on  client   feedback.   •  Data     –  Client  provided  ~3,000,000  phrase  pairs.     –  26%  were  rejected  in  cleaning  process  as  unsuitable  for  SMT  training.   •  Measurements:   –  Cost   –  Timeframe   –  Quality  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  40. 40. •  Quality   –  Client  performed  their  own  metrics   –  Asia  Online  Language  Studio™  was   considerably  beher  than  the  clients   own  MT  solu@on.   –  Significant  quality  improvement  aGer   60%  Cost  Saving   providing  feedback  –  65  BLEU  score.   –  Chinese  scored  beher  than  first  pass   human  transla@on  as  per  client’s   feedback  and  was  faster  and  easier  to   edit.   •  Result     70%  Time  Saving   –  Client  extremely  impressed  with  result   especially  when  compared  to  the   output  of  their  own  MT  engine.   –  Client  has  commissioned  Sajan  to   work  with  more  languages   LRC  have  uploaded  Sajan’s  slides  and  video  PresentaCon  from  the  recent  LRC  conference:   Slides:  hLp://bit.ly/r6BPkT            Video:  hLp://bit.ly/trsyhg  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  41. 41. Travel  &  Leisure  Ver@cal   English  to  Spanish  Language  Pair   Custom  MT  engines  built  and  programma@cally  consumed     A  human  post  edit  step  was  included  in  workflow  and   measurement   Scien@fic  measures  of  produc@vity  for  all  phases  of  process  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  42. 42. Base  training  materials  provided  and  catalogued   Asia  Online  trained  the  engine  and  released  to  a  diagnos@c   stage   First  pass  of  new  content  through  diagnos@c  engine  yielded   posi@ve  results   Asia  Online  provided  advanced  data  genera@on  technologies   to  the  diagnos@c  engine  through  monolingual  data  crawling,   applica@on  of  run@me  rules,  and  pre-­‐transla@on  adjustments     Even  further  progress  achieved  from  extrac@ng  and  applying   a  industry  specific  high  frequency  term  list  from  the  source  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  43. 43. 58%  of  segments   required  no  edits  Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  44. 44. Post  Edit  Produc<vity  Analysis   Produc@vity  Percentage   328%  Increase   Produc@vity  Rate   8,208  words  a  day        Copyright  ©  2013,  Asia  Online  Pte  Ltd  
  45. 45. Business  Strategies  for  Building  Strategic  Advantage  and  Revenue  from   Machine  Transla<on   Dion  Wiggins   Chief  Execu<ve  Officer   dion.wiggins@asiaonline.net    Copyright  ©  2013,  Asia  Online  Pte  Ltd  

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