EAMT 2015 special invited guest Olga Beregovaya, VP of Language Tools and Automation at Welocalize, presented "What we want, what we need, what we absolutely can’t do without." This presentation is an enterprise user’s perspective on machine translation technology and stuff around it. Olga Beregovaya has over 15 years of experience in the localization industry. She started her career managing RBMT domain adaptation projects. Later she managed Language Quality at Autodesk, where she oversaw the implementation of LQA automation tools and selection of MT solutions. In 2008 Olga moved to PROMT where she was in charge of the company's enterprise deployments. Since 2011, Olga is driving the Language Technology strategy for Welocalize. Olga is in charge of the development of in-house content curation tools, a post-editing platform and adaptation of the Moses SMT toolkit. Olga is a frequent presenter at various industry conferences. http://www.eamt2015.org/en/INVITED-SPEAKER.html http://www.welocalize.com

1. What We Want, What We Need,
What We Can’t Do Without
The Enterprise User Perspective on MT Technology & Things Around It
Olga Beregovaya
VP, Language Tools

2. ONE THING HAS BECOME ALL THINGS; EVERYTHING
• Global Economy Multicultural Transactions
• Multiple Demand-Driven Content Scenarios
• Multiple Data Sources + Formats

3. 27,500 words - 25,000 emoji lattices
…” And what is the use of a book,” thought Alice, “without pictures or
conversations?”
?

4. “The limits of my language means the limits of my world.”
― Ludwig Wittgenstein
“Never send a human to do a machine’s job.”
― Agent Smith

5. •Areas of utmost interest and importance as seen from a major Language
Service Provider perspective, or “a week in a life of an Language Technology
group”
•Engine Output Quality; what is it actually, “output quality”? Are we “stuck”?
Next breakthrough?
•Domain adaptation, what can we do when there is neither data nor
budget to create it?
•Supporting “Raw” publishing scenarios (UGC, Support, MT to be consumed
by other applications) – there will be no human to fix it. Or will there?
•Metadata – an enemy or an ally?
•Collaboration: how can we make it interesting for everyone?
AND WE’LL TALK ABOUT…

6. • What do translators appreciate?
• What do translators struggle most with?
• Fluency VS. Accuracy?
• Final output quality?
WINS & CHALLENGES

7. CONTENT DRIVING
QUALITY DECISIONS
LEVELS OF
POST-EDITING
CONTENT TYPE STRATEGY
FULL POST- EDITING
Content that meets certain
“impact” criteria-visibility, number
of clicks, “shelf life”
Post-edit to human translation
levels, correct for terminology,
grammar, fluency, style and
voice
MEDIUM POST-EDITING
Human translation level
requirement but with flexible
style and fluency allowances
Content that meets certain
“impact” criteria-visibility, number
of clicks, “shelf life”
LIGHT POST-EDITING
Emphasis on quick turnaround
and/or large volumes
Sliding scale depending on the
content purpose

8. Si queréis viajar en grupo,
sóis al menos 6 personas
y deseáis ir en otras
fechas diferentes
a las propuestas,
preguntarnos porque lo
podemos organizar
;-)
If there are at least 6 of
you wishing to travel
together, but on different
dates that the ones
offered, why don’t you ask
us? We can arrange it. ;- )
If you want to travel in a
group, you are at least 6
people and you wish to
travel on different dates
other than the proposed
ones, let us know because
we can organize it.
If you want to travel in a
group, you are at least 6
people and you wish to
travel on different dates
other than the proposed
ones, let us know because
we can organize it.
If you want to travel in
groups , are at least 6
people and wish to go on
dates other than the
proposed, ask us
because
we can organize ;- )
If you want to travel in
groups , sóis at least 6
people and wish to go on
dates other than the
proposals, ask why we
can organize ;- )
A la hora de generar las
cuotas de amortización es
posible utilizar dos
porcentajes distintos; el
fiscal o el de mercado que
la empresa establezca.
At the time of generating
amortization fees, one
might use two different
percentages: Fiscal or
Market established by the
company.
When generating the
amortization fees, it is
possible to use two
different percentages; the
fiscal percentage and the
company established
market one.
When generating the
amortization fees, it is
possible to use two
different percentages; the
fiscal percentage and the
company established
market one.
When generating the
amortization fees is
possible to use two
different percentages; the
fiscal or the market rate
that the company stated.
When generating the
repayment is possible to
use two different rates;
the prosecutor or the
market that the company
stated.
Para el supuesto de que
haya contratado el
servicio de
actualizaciones relativo al
programa software objeto
de licencia, usted podrá
actualizar el mismo
durante los periodos de
vigencia que tenga
contratado el servicio.
You can update the
licensed software during
the period stated in your
contract.
In case you have signed
up for the update service
related to the licensed
software, you will be able
to update the software
during the validity periods
stated in the contract.
In case you have signed
up for the update service
related to the licensed
software, you will be able
to update the software
during the validity periods
stated in the contract.
Because you engaged
the update service related
to the licensed software
program, you can update it
during periods of validity
of the service contracted
For the assumption that
engaged the update
service software on the
licensed program, you can
update it during periods of
validity has contracted the
DOMAINS
SOURCE
TRANSCREATION
TRANSLATION
FULL
POST-EDITING
LIGHT
POST-EDITING
RAWMT
TRAVEL
FINANCE
LEGAL
CONSUMING MT — QUALITY SCENARIOS

9. THE POST-EDITOR PRODUCES:
Publishable quality
The post-editor is responsible for ensuring that client quality requirements
and style guide are met
The post-editor is expected to adhere to client StyleGuide preferences
with regard to:
Infinitive / Imperative
Passive / Impassive
Formal / Informal
Different Styles for Headers, Lists, Tables
Special Handling of UI Options (Bilingual, English, Target?)
Converting All the Measurements Based On the Local Conventions
+ Disambiguate Terminology
+ Correct all the grammatical errors

10. THE POST-EDITOR RECEIVES:
GERMAN FRENCH JAPANESE RUSSIAN CHINESE SPANISH ITALIAN BRAZILIAN
WRONG TERMINOLOGY 6.46 4.93 13.63 5.00 6.20 9.63 3.78 1.13
WRONG SPELLING 2.00 0.86 0.88 0.13 0.30 1.13 0.56 1.27
SOURCE NOT TRANSLATED 6.38 5.36 3.88 5.13 3.60 2.50 1.22 1.73
COMPLIANCE WITH CLIENT
SPECS
2.46 0.86 3.00 2.13 0.70 0.63 0.44 2.60
LITERAL TRANSLATION 7.85 8.64 5.00 4.00 9.40 5.38 7.67 7.93
TEXT/INFO ADDED 2.69 1.36 2.13 1.25 0.80 1.88 0.44 0.80
CAPITALIZATION 2.69 3.43 0.00 2.63 0.50 1.75 3.33 2.60
WRONG WORD FORM 6.77 7.79 0.13 9.88 0.60 6.75 3.67 6.75
WRONG PART OF SPEECH 2.62 3.21 2.00 1.88 0.60 2.13 3.67 1.33
PUNCTUATION 4.46 3.00 0.75 3.38 4.10 2.13 1.22 3.53
SENTENCE STRUCTURE 12.54 10.00 14.25 8.00 13.00 5.38 6.11 3.67
TAGS + MARK-UP 1.23 0.14 0.13 0.50 0.20 0.38 0.44 0.20
LOCALE ADAPTATION 0.46 0.29 0.75 0.63 0.20 0.75 0.44 0.13
SPACING 0.92 0.36 2.25 1.25 4.00 0.50 0.33 0.40
OTHER 1.92 1.50 1.88 0.13 0.50 0.13 1.44 0.27
TOTAL ERRORS 61.46 51.71 50.63 45.88 44.70 41.00 34.78 32.53

11. Most time-consuming issues that translators need to
fix are:
• Sentence structure (word order)
• MT output too literal
• Wrong terminology
• Word form disagreements
• Source term left untranslated
OR, IN A NUTSHELL…

12. TOP 6 ON THE TRANSLATORS’ LOVE IT-LIST
1. Source of inspiration: reduces thinking and translation choice time
2. Provides reference - very useful to translators new to a specific domain
3. Reduces typing & lookup time by handling well repetitive terminology and
structures
4. …thereby takes away the more monotonous efforts of translation
5. Post-editors over time notice improvements; appreciate it more if they
‘co-own’ the engine
6. MT output can be funny
LOL!

13. TOP 3 ON THE TRANSLATORS’ S*#!T-LIST
1. Wrong sentence structure
• Major impact on the post-editing effort (Spanish and Portuguese produce fewest errors)
• Japanese has the highest error rate and the lowest productivity gains (supported by
the cognitive effort error ranking research)
2. Wrong and inconsistent terminology
• Very time-consuming to check and fix terminology; + enough issues from Fuzzy
Matches already
• A major problem for new products where the terminology is not settled yet
• Inconsistent output for UI references
3. Correct MT to an agreed standard (=quality expectations)
• A challenging concept in the beginning for post-editors – they think they should edit
less if the quality is bad
S*#!T

14. FEEDBACK LOOP
SOURCE TEXT MT OUTPUT POST-EDITED OUTPUT
SPECIFIC
ERRORS/CHANGES MADE
Single-phase options range from 1.4kW
to 7.7kW while three-phase PDUs,
packed with output receptacles, range
from 8.6kW to 21.6kW.
Single-fase 7.7kW Opties variëren van
1.4kW om en driefasige PDU's, boordevol
Output-aansluitingen, variëren van 8,6
kW tot 21.6kW.
1,4 kW ... 7,7 kW ... 21,6 kW
Numbers and measurement units are not
converted properly and no spaces
inserted by MT engine (3 out of 4
occurrences, 1 is correct however,
strange...
Single-phase options range from 1.4kW
to 7.7kW while three-phase PDUs,
packed with output receptacles, range
from 8.6kW to 21.6kW.
• Biedt maximaal 24 TB <fmt id="1"
tooltip="SUPERSCRIPT"
endtooltip="SUPERSCRIPT"> 2 </fmt>
maximale capaciteit per-
uitbreidingsbehuizing toe te voegen.
• Biedt een maximale capaciteit van 24
TB<fmt id="1" tooltip="SUPERSCRIPT"
endtooltip="SUPERSCRIPT">2</fmt> per
uitbreidingsbehuizing.
No space should be inserted in front of
and behind a number in superscript (in
this case a "2"). ...>2<... and not: > 2 <
<fmt id="1" tooltip="b"
endtooltip="b">Interface Speed:</fmt> 6
Gb/s SAS
<fmt id="1" tooltip="b" endtooltip="b">
Interfacesnelheid: 6 </fmt> Gb/s SAS
• Biedt een maximale capaciteit van 24
TB<fmt id="1" tooltip="SUPERSCRIPT"
endtooltip="SUPERSCRIPT">2</fmt> per
uitbreidingsbehuizing.
The number is inserted before the tag
and should be after the tag
<fmt id="1" tooltip="b"
endtooltip="b">Intermixed Drive
Capacities:</fmt> Yes
<fmt id="1" tooltip="b" endtooltip="b">
Intermixed Capaciteit van de schijven:
Ja </fmt>
...</fmt> Ja
The string is inserted before the tag and
should be after the tag (and again
spacing before and after tags inserted)
A new feature — DR Rapid Data Access
— adds tighter integration with backup
software applications, starting with
Symantec OpenStorage-enabled
backup applications.
Een nieuwe functie - DR-Rapid Data
Access - voegt strakkere integratie met
back-uptoepassingen, beginnend met
Symantec OpenStorage geschikte
back-uptoepassingen.
... — DR Rapid Data Access — ...
Please ensure any special characters
like — (ChrW(151)) are preserved when
inserting a TM proposal, and not
replaced by a normal hyphen
(ChrW(45)).
Can these errors can be learned and corrected automatically? Can
we simplify or omit the “feedback loop”?

15. • How much more can we squeeze out of SMT phrase-based systems?
• Factored models?
• Deep syntactic/semantic structures?
• Have a closer look at rule-based systems?
• Deep Learning?
TRANSLATION QUALITY

16. QUALITY DEGRADATION WITH POST-EDITING?

17. POST-EDITING QUALITY RESULTS
No fails on one of our 28-language PE program thanks to correct
terminology choices and few and consistent error.

18. DOMAIN ADAPTATION
• How much can we get out of minimal amounts of data? A little more
data? Mixed-domain data?
• Forcing dictionaries -fluency vs. adequacy? How can we seamlessly
integrate client/user dictionaries into standard SMT workflows?
• How often to retrain?
• Does dynamic/interactive/”live” retraining help solve the domain
relevance problem?

19. “History is filled with brilliant people who wanted to fix things and just
made them worse.”
― Chuck Palahniuk

20. CONTENT EXPLOSION

21. HOW USEFUL IS MT FOR UGC?
•We performed an evaluations after normalization and domain
customizations of SMT engines.
•Between 54% and 96% of travel reviews scored between 3 and 5
on the Utility scale.

22. WHY DO WE CARE?
BACKPACKER WEBSITE REVIEWS
LUXURY HOTEL REVIEWS
TECHNICAL FORUM
Translation purpose: youthful, 5 locales, cheap
Translation purpose: attract high-end clientele in 1 particular
target market
Translation purpose: save cost on user support, as many
locales as possible
MT + normalization; paid crowd
with basic instructions on “do’s +
don’ts”; crowd can be mix of
translators / customers / …
possibly MT with Full PE, but
possibly professional HT /
transcreation
MT +”accuracy check” PE; crowd
of technical users, savvy on product,
linguistic errors are ok

23. Global Commerce Global Consumer Pandora’s
Box of Brand Names and Geographic Locations

24. SOURCE CONTENT GONE WILD
• Short forms (nite (night), sayin (saying), gr8 (great)),
• Acronyms (lol (laugh out loud), iirc (if I remember correctly)),
• Typing errors/misspellings (wouls (would), rediculous (ridiculous)),
• Punctuation omissions/errors (im (I’m), dont (don’t)),
• Non-dictionary slang (that was well mint (that was very good)),
• Wordplay (that was soooooo great (that was so great)),
• Censor avoidance (sh1t, f***),
• Emoticons (:) (smileys), <3 (heart))
• Foreign words used intentionally (al dente, bon voyage)
(Jiang et al, 2012; Clark & Araki, 2011)

25. TRAVEL USER REVIEWS
Non-native writers, typos, grammar errors, two authors with
completely different styles & opinions, idioms don’t make sense…
this is our source text.

26. NORMALIZING TRAVEL CONTENT
Any other normalization techniques?

27. ONLINE RETAILER GLOBAL LISTINGS

28. BEHIND THE SCENES
Proper Name Ant Farm
SOURCE MT OUTPUT
Basic MIDI Applications (Keyboard Magazine Library for
Electronic Musicians), Ca
Grundlegende MIDI-Anwendungen (Tastatur Magazin
Library für elektronische Musiker), Ca
Analog Way smart cut 2 seamless video & computer
switcher - hi res scaled output
Analoge Weise smart cut 2 nahtlose Video & Computer-
Umschalter - Hallo Res skalierte Ausgabe
TOO FAST TOP RAT BABY FANG VAMPIRE LIPS LEOPARD
ROCKABILLY PINUP USA M IRON FIST
ZU SCHNELL TOP RATTE BABY FANG VAMPIR LIPPEN
LEOPARD ROCKABILLY PINUP GIRL USA M IRON FIST
YRU Youth Rise Up Kreep Platform Stacked Leopard Animal
Suede Spike Studded Pump
YRU Jugend Aufstieg Kreep Plattform gestapelt Leopard Tier
Wildleder Spike beschlagene Pumpe

29. DO WE WANT TO MESS WITH THIS THING?
• Words missing in the target / extra words
• Terms are translated with different capitalization within the same message
• Incorrect positive / negative translation
• Lack of fluency
• Mix of formal and informal form of address
• Wrong translation for the context

30. WHAT IS “QUALITY” FOR A GERMAN TOWER
CRANE OPERATOR?
• Do I close or not close the valve
before re-pressurizing?
• Do you mean a wheel or the pulley?

31. Punctuation and numbers:
• Handling locale-specific punctuation ‘Security Systems” to「セキュリティ システム」
• Slashes -space or no space on/off to вкл / выкл
• Number formatting, i.e. trailing characters 33 % or 33%?
• Target language/locale numbering conventions 44,500 to 44.500
• Intelligently match punctuation, i.e. remove unmatched quotes and parenthes
• Keep source capitalization or replace with target language capitalization conventions?
• Translate or transliterate addresses based on target language conventions
• OOV handling? Leave in the source language or Transliterate (flag for the user?)
• Handling DoNotTranslate without breaking the flow of the target sentence
• Handling Acronyms – does it expand? Which part of it is in the parentheses?
• Recognize groups as proper names (Herr Vogel is not a bird!)
A HUGE ONE: Preserving the negative or positive meaning ("Do remove this part" vs. "Do not
remove this part”); handling standalone negation in source with affixed negation in the
target
AN EQUALLY HUGE ONE: language identification – file level and even more so sub-
file/sentence level
UGC SHOPPING LIST

32. WORKFLOW, FORMATTING + METADATA

33. QUIZ: COUNT THE INTEGRATION

34. ‘Segment’ vs. ‘Sentence’
A segment can be a lot of things - a sentence, a part of a sentence, a
word , so if the engine is integrated on a “segment level”, ellipse,
anaphora, other context-related features will not be taken into account
Doesn’t take much to break segmentation: a line break, a carriage return
or anything ambiguous will do the job – damage both on the training and
the runtime side
WHAT IS A ‘SEGMENT’?

35. “A camel is a horse designed by a committee.”
― Sir Alexander Arnold Constantine Issigonis

36. LOCALIZATION TAG PLACEMENT
This is what a plain-text engine will do:
To become verified and lift your sending limit, please confirm your email
address, then add a credit or prepaid card to your account and {30} {31}
{32} {33} {34} {35}confirm{36} {37} {38} it.{39}.
{30}Para hacerse verificado y levantar su límite de envío, por favor
confirme su dirección de correo electrónico, luego añada un crédito o
tarjeta de prepago a su cuenta de y
confírmelo.{31}{32}{33}{34}{35}{36}{37}{38}{39}

37. This is a<ph id="1" x="&lt;b&gt;">{1}</ph>test<ph id="1"
x="&lt;/b&gt;">{2}</ph>
Dies ist ein <ph id="1" x="&lt;b&gt;">{1}</ph>Test<ph id="1"
x="&lt;/b&gt;">{2}</ph>.
AND THIS IS WHAT’S NEEDED

38. TAG PROJECTION TECHNIQUES?
•We’ll be happy to
consume more
information, but then
please expose more
information
•Walls, zones, pre-
processing, post-
processing – can we do
more?

39. AN ENEMY OR AN ALLY?
• Domain, sub-domain, product
• Timestamps - deprecate TU-s in the training data?
• XLIFF metadata fields that carry information about specific terms
• UI strings and other variables markup
• Annotation fields
Can Localization metadata can be helpful for MT?

40. WE WANT TO SHARE
• Human evaluation and ranking
• Source/MT/Edits corpora (for experimentation only)
• Productivity data per-segment (side-by-side with PE distance
and other metrics) – thanks iOmegaT
• Database of correlations between automated scoring, human
ranking and PE effort and time
• Data on correlation between specific errors and translator
preference – can it help translator-focused confidence
scoring?
We have A LOT of "field" data:

41. DATA
Statistics from
internal database

42. CORRELATION RESULTS
Adequacy & Fluency versus Productivity Delta
Productivity and Fluency across
all locales with a cumulative
Pearson’s r of 0.77, a very
strong correlation
Productivity and Adequacy
across all locales with a
cumulative Pearson’s r of 0.71,
a very strong correlation
According to our data, Human Evaluations are stronger
predictors of post-editing productivity gains than Automatic
metrics including PE distance

43. CORRELATION RESULTS
Automatic Metrics versus Productivity Delta
Productivity delta and BLEU with
a cumulative Pearson’s r of
0.24, a weak positive
relationship
With a Pearson’s r of -0.436,
as PE distance increases,
indicating a greater effort from
the post-editor, Productivity
declines; it is a strong negative
relationship

44. • More transparency in workings of engine and training
• Faster systems, shorter turnaround on large systems
• More “wizards” for training and deployment
• Easier testing methodologies without full deployments
• More standardized scoring and comparison metrics
• More “wizards” for training and deployment
• Predictive analysis of quality – confidence and utility scores
• Normalization integrated into workflow and standardized
• Industry-wide proper name and title library
• Better transliteration standards
• Morphologically aware terminology choices
• More research on post-editing environments
1. How to display source/target
2. How to display multiple suggestions
3. Autocomplete
4. Better ways to calculate the productivity improvements with post-editing
• More interoperability, so translators can stay in CAT tool they prefer
• Simplified workflows connecting MT engines and other tools
Dear Santa,

45. SHALL WE?
— Napoleon Hill

46. Thank you.