Descripción del funcionamiento de una empresa de traducción, departamentos y procesos, tomando a www.pangeanic.es como ejemplo. Descripción de funciones, normas y flujo de trabajo con un énfasis en los procesos de traducción automática.

1. Pangeanic PangeaMT
Gestión de proyectos
de
Traducción
Automática

2. Why machine Translation?

3. Why machine Translation?

4. MT Usage
MT for assimilation: “gisting” or
“understanding“
• Practically unlimited demand; but free web-based
services reduce incentive to improve technology
• Coverage + important. Instant quality
MT for dissemination: “publication“
MT for direct communication
• Publishable quality that can only be achieved by
humans. MT & tools a productivity booster
• Current R&D, Military uses systems
for spoken MT, first applications for
smartphones

5. Machine translation
Language Model (Fluency)Translation Model (Adequacy)
Monolingual
Corpus
Phrase
Table
Parallel
Corpus
nGram-
Model
Alignment,
Phrase
Extraction
Counting,
Smoothing
Decoder
(translator)
Source
Text
Target
Text
N-best
Lists
Basic Architecture for Statistical MT

6. MT@DGT
A selection of 23 out of 3897 ways to translate operations from EN to FR
operations ||| action ||| (0) ||| (0) ||| 0.00338724 0.0017156 0.00316685 0.0034059 2.718
operations ||| actions ||| (0) ||| (0) ||| 0.0575431 0.0534003 0.0731052 0.0958526 2.718
operations ||| activité ||| (0) ||| (0) ||| 0.0102038 0.0079204 0.00744879 0.0084917 2.718
operations ||| activités ||| (0) ||| (0) ||| 0.019962 0.0194538 0.0366753 0.0451576 2.718
operations ||| des actions ||| (0,1) ||| (0) (0) ||| 0.0304499 0.0269505 0.00973472 0.00438066 2.718
operations ||| des activités ||| (0,1) ||| (0) (0) ||| 0.00877089 0.00997725 0.00246435 0.00206379 2.718
operations ||| des opérations ||| (0,1) ||| (0) (0) ||| 0.294821 0.281318 0.0406896 0.0238681 2.718
operations ||| exploitation ||| (0) ||| (0) ||| 0.0437821 0.0365346 0.0208856 0.029298 2.718
operations ||| fonctionnement ||| (0) ||| (0) ||| 0.0141471 0.01165 0.00919948 0.0099513 2.718
operations ||| gestion ||| (0) ||| (0) ||| 0.00141338 0.0013098 0.00286578 0.0032669 2.718
operations ||| intervention ||| (0) ||| (0) ||| 0.00561479 0.0026006 0.00110394 0.0013554 2.718
operations ||| interventions ||| (0) ||| (0) ||| 0.0830237 0.0778631 0.0102142 0.0149096 2.718
operations ||| les actions ||| (0,1) ||| (0) (0) ||| 0.0339458 0.0271478 0.00931099 0.00712787 2.718
operations ||| les activités ||| (0,1) ||| (0) (0) ||| 0.00915348 0.0101746 0.00296613 0.00335805 2.718
operations ||| les interventions ||| (0,1) ||| (0) (0) ||| 0.0565693 0.0393793 0.00207406 0.00110872 2.718
operations ||| les opérations ||| (0,1) ||| (0) (0) ||| 0.413399 0.281515 0.0564235 0.0388363 2.718
operations ||| manipulations ||| (0) ||| (0) ||| 0.0985325 0.183951 0.00104818 0.0034523 2.718
operations ||| operations ||| (0) ||| (0) ||| 0.786026 0.557952 0.00200716 0.0023981 2.718
operations ||| opération ||| (0) ||| (0) ||| 0.0245776 0.021675 0.00785022 0.0085959 2.718
operations ||| opérationnel ||| (0) ||| (0) ||| 0.00656403 0.0069192 0.0012266 0.0013902 2.718
operations ||| opérations effectuées ||| (0,1) ||| (0) (0) ||| 0.110801 0.285316 0.00132696 0.00229301 2.718
operations ||| opérations ||| (0) ||| (0) ||| 0.636821 0.562135 0.409237 0.522254 2.718
operations ||| travaux ||| (0) ||| (0) ||| 0.00273044 0.0024213 0.00194025 0.0023517 2.718
Examples of Statistical MT

7. Unrest is continuing in Cairo as protesters set up their demand for Egypt’s
military rulers to resign
How does Statistical MT work?

8. • SMT can be seen as a generalisation of Translation
Memory to sub-segmental level
• The phrases are text snippets (n-grams) taken from real-
world translations (= as good as the source you entered)
• Re-combination of those phrases for out-of-context
translation may lead to significant problems :
– Alignment errors spurious/lost meaning
– Bad morphology (declensions)
– Grammatical errors
– Wrong disambiguation
• SMT will not recover implicit information from source text
nor handle structural mismatches, it needs more info.
}
SMT from a translator’s perspective?

9. Strengths and Weaknesses of pure SMT
(RBMT:translate pro SMT:Koehn 2005, examples from EuroParl)
EN: I wish the negotiators continued success with their work
in this important area.
RBMT: Ich wünsche, dass die Unterhändler Erfolg mit ihrer
Arbeit in diesem wichtigen Bereich fortsetzten.
continued: Verb instead of adjective
SMT: Ich wünsche der Verhandlungsführer fortgesetzte Erfolg
bei ihrer Arbeit in diesem wichtigen Bereich.
three wrong inflectional endings
This has consequences in the fair PE payment model

10. Strengths and Weaknesses of pure SMT
In the early 90s and early
00’s, strong support and
advocacy for either of the
two technologies: SMT vs
RBMT.
But we have learn to
combine the best of both
worlds. Google ditched
Systran (Och, 2005), went
all statistical and now SMT
plus linguistic models.
Microsoft (Chris Wendt)
also uses 2 technologies

11. Examples of Hybrid MT architectures
= SMT Module
= RBMT ModuleThe search for integrated (hybrid) methods is now seen
as natural extension of both approaches

12. How does PangeaMT work?
It can bring together syntactically distant languages from
different families
This has consequences in the fair PE payment model

13. SYNTAX (LINGUISTIC)-BASED HYBRID SMT
When available, the company plans to offer the following:
available When , the company the following : plans to offer :
(VBPt3) (to) (VBinf) (DET)
(NN)
(Predicate)
Nipponization module
Translation & Cleaning
(Subject) (VBPt) (to)
(ADV) (ADJ)
(Punct)
(DET)
(NNSing)
(Cond clause),
How does PangeaMT work?

14. How do you manage a translation project?

15. How do you manage a translation project?
Typical order workflow at an LSP
http://pangeanic.es/norma-calidad-en15038-gestion-de-ofertas-pedidos-y-contratos/

16. How do you manage a translation project?
EN15038 - Typical translation job project management at a translation
company

17. How do you manage a translation project?
• Main tool for language professionals
• TMs-based retrieval
• Shows similar (hight percent match)
translations stored in the TM for each
segment of translation
• 100% matches: perfect, identical
segments from the TM
• Fuzzy matches: there are no 100%
matches, but there are similar
segments. Generally, matches below
70% require full human “PE”.
• Mean productivity: 2000-3000 words/
day. E.g.:
• Proprietary software: SDL Trados,
MemoQ, Deja Vu, Memsource
• Semi-free: WordFast
• Opensource software: OmegaT
Advantages:
Higher productivity in professional
translation
Very popular, low-entry threshold, not
so expensive
Friendly GUI
Easy Quality Check (QC)
Latest versions: can integrate MT as
suggestion
Disadvantages:
Translation is not scalable = production
is directly related to available
translators/hour/day
If there isn't any TM available, there is
no productivity improvement

18. How do you manage a translation project?
Advantages:
Translation becomes scalable, millions
of words can be translated per day
More productivity in non-professional
translations
Disadvantages:
A lot of data is needed to create a
engine (SMT)
A lot of time is needed to create an
engine (RBMT)
MT alone is not useful for professional
translation, only for gisting
• Production enhancement tools
• Full, complex systems
• Multilanguage
• Full translation
• “Gisting”
• Translate content which otherwise
would be ignored, not accessible or
not understood.

19. How do you manage a translation project?
• Already available in the 90s between
Trados and Systran
• The CAT tool shows similar translations
from TM and a suggestion or
suggestions from MT, and the user
chooses what to postedit.
E.g.:
translate segments with matches
above 70-%75% with TM
translate segments with matches
below 70-75% with MT
• If custom-built, very good results, but
professional translators are reluctant to
become post-editors (price pressures)
• Average productivity: +5000 words/day
Advantages:
Postediting is faster than translating
from scratch
The future of the translation industry?
Enable faster, HQ translations
Fast postediting if the MT output
reaches certain Q levels
Disadvantages:
If the MT output is bad, better to
translate from scratch

20. How do you manage a translation project?

21. How do you manage a translation project?

22. How do you manage a translation project?
+: Direct
- : PE slow (can't use CAT tools)
QC difficult (cannot use XBench or QA Distiller)

23. How do you manage a translation project?

24. How do you manage a translation project?
+: Fast if we have no TM
QC difficult (cannot use XBench or QA Distiller)
- : Slightly slower (minutes) if we have TM as file
translation is a checking/retrieval exercise

25. How do you manage a translation project?
How it works:
- During pre-analysis, export unknown segment (segments under XX percent match) into bilingual file
- Translate bilingual file with MT
- Import MT translated bilingual le into the TM
- Select a penalty for MT
- We can choose TM or MT (like a “bad TM”)
+ :
Fast postediting
- :
A few steps more to do preparing the project

26. How do you manage a translation project?
How it works:
-
- Import MT translated bilingual le into the TM
- Select a penalty for MT
- We can choose TM or MT suggestion
+ :
Fast post-editing
- :
If internet-based, latency depends on the Internet

27. How do you manage a translation project?

28. Automated engine training

29. Statistics and data are paramount for PMs

31. The post-editor’s dilemma
CLIENT SEES
NEEDS
OMG!!!! BUT I AM A PROFESSIONAL

32. EXERCISE
Work in pairs, one is PM, one is “expert translator”:
PM tries to convince the translator to accept a lower rate for large volume of work (say 5000 words) to be
done in one day as post-editor of an already pre-translated text.
PM sees the aver on a “day salary basis”, offering a likelihood of the text being OK and spending half of the
time the translator would spend, so the translator earns the same in terms of time
TRANSLATOR says MT still requires reading, understanding, correcting, sometimes needs to be done from
scratch.
Use your own experience with online tools
SHARE THE RESULTS WIT THE TEAM / WHO WINS ? / WHAT LESSONS HAVE YOU LEARNT?

33. PangeaMT
Thanks!!
Questions?