Machine Translation Prof. Andy Way - What it can and can't do for Translators
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Machine Translation
What it can and can't do for Translators
Prof. Andy Way
away@computing.dcu.ie
http://www.computing.dcu.ie/~away
ASTTI Conference, Berne, Oct 24th 2014
Agenda ● Introduction ● What is MT able to do today? ● Impacts on Translators ● What is MT not able to do (yet)? ● The Way Forward
Andy Way: a potted history
● a linguist, programmer and computational linguist
● a senior academic
– Professor of Computer Science
– Deputy Director of CNGL/ADAPT
– Leader of a large, globally well-renowned MT team
● ex-owner of a translation company
● leader of large industry-facing organisations
● ex-director of industry-leading MT/LT groups
● editor of the leading journal in our field
● >25 years of experience in building MT engines
What can MT do today? ● What types of systems? ● What types of use-cases? ● What success stories? ● How are translators involved?
Rule-Based MT
C
o
l
m
u
n
3
2
1
The Vauquois Pyramid
The creep of corpus-based MT: why? (Relative) failure of RBMT, Increasing availability of digitised text, Increase in capability of hardware (CPU, memory, disk space) with corresponding decrease in cost.
Types of corpus-based MT Example-Based MT (Nagao, 1984) Statistical MT 1988: word-based (IBM) 2002—now: phrase-based (Moses) 2005—now: tree-based (Hiero)
Parallel data prerequisite for corpus-based MT
Parallel data prerequisite for corpus-based MT
What has helped SMT development? Open-Source tools (Moses, Giza++, IRSTLM) Automatic Evaluation Metrics (BLEU, TER) International Bake-offs (NIST, WMT, IWSLT)
MT is being used every day ...
● Provides a billion translations a day for 200 million users
● 92% of the usage is from people outside the US
● Amount of text translated daily is more than what's in a million
books
● Surpasses what professional translators handle in a full yearClient-customised engines
● Improve productivity,
● Translate content previously not feasible due to
time or cost constraints,
● Reduce time to market,
● Reduce translation costs.Traditional Use-Cases for MT ● Raw MT, ● MT with light post-editing, ● MT with full post-editing.
Lots of successful case studies ...
● Adobe & ProMT
● Church of Jesus Christ of Latter-day Saints &
Microsoft Translator Hub
● Dell & Safaba/welocalize
● DuDu & CapitaTI
● Ford & Systran/SAIC
● Sajan & Asia Online
● text&form & LucySoftContent production is changing (fast)
● Early adopters of MT mostly large software companies and media
organisations. Unsurprising given their near monopoly on data not
so long ago.
● “With the advent of Web 2.0, individual users have been able to
actively participate in the generation of online content via
community forums or social media … the Web [is now] open and
accessible to an ever-larger percentage of the world’s population.”
(Jiang et al., 2012:1)User-Generated Content
● Online chat (cf. Jiang et al., 2012a), tweets, blogs,
hotel or product reviews, social media posts etc.
● Most of this data is extremely perishable, e.g. as soon
as MT has facilitated online chat between speakers of
mutually unintelligible languages, the data has no
further purpose and may immediately be deleted.
● Might not have the budget for professional translation
of such data, but having it available in a range of
languages gives tremendous added value on company
websites.Challenges in translating UGC ● No (client-specific) parallel data, ● Informal linguistic style, ● Contains all sorts of misspellings and abbreviations, ● Fast, real-time translation.
'Less disposable' UGC
● Forum translation (Banerjee et al.,
2012),
● Translation of content in online
games (Penkale & Way, 2012),
● Translation of eBay product
listings (Jiang et al., 2012b).Other emerging use-cases
● Verification of potential user demand on multilingual
website versions,
● Translation of course syllabi documentation and other
educational information (cf. Bologna FP7 project),
● 'DIY' approaches to MT.The changing role of the translator
“Given the challenges they face in their day-to-day work, most
human translators today would acknowledge the critical role of
technology in their workflow. However, it is fair to say that some
of this technology is more highly regarded than others. For
example, most translators are happy to use Translation Memory
tools (Heyn, 1998), while Machine Translation has met with
much less widespread acceptance to date.”
(Bota et al., 2013)Translators love TM ... ● ... at least they do now.
Translators love TM ...
● ... at least they do now. Is that justified?
● As a productivity tool it's limited, certainly
compared to the potential gain with MT.
● Why do translators love TM, then?
– They trust it,
– They have confidence in it,
– They've put investment into it,
– It's predictable: if you ask for all matches above a
75% fuzzy match, that's what you'll get.Combining TM and MT
● Seamlessly integrated, complementary
● 100% and ICE matches come from TM
● Even if MT engines are not retrained after every
job, post-edits can be automatically reinserted
into TM for increased future leveraging
● MT outputs can be presented as alternative
high-scoring fuzzy matches, with matches
between MT and best fuzzy highlightedCombining TM and MT
● Usually, arbitrary cut-offs at different fuzzy match
levels
● Research has demonstrated that TM can be better
than MT at sub-70% matches, and MT can be better
than TM at over 70% matches
● More informed ways of combining the two:
http://www.cngl.ie/wp-content/uploads/2013/04
/TMTPrime_Leaflet.pdf(Some) translators hate MT ... but
also (some) translators!
“Translation commentators lead the field in
throwing most of its work in the direction of the
garbage dump ... it seems implausible that anyone
would ever make such a statement about any other
human skill or trade.” (Bellos, 2011: 329)Why do (some) translators hate MT so much?
“To me, this was a moment of enlightenment in the
book, although probably not one intended (and
certainly not mentioned) by Bellos: at last, translators
have something else to pick on, namely MT! They are
so inured to this level of talking about translation, that
they naturally use it against us.”
(Way, 2012: 260—261)But there's a lot that MT can't do (yet)! We gave the monkey the banana as it was ...
But there's a lot that MT can't do (yet)!
We gave the monkey the banana as it was ...
● ripe (sie)But there's a lot that MT can't do (yet)!
We gave the monkey the banana as it was ...
● ripe (sie)
● teatime (es)But there's a lot that MT can't do (yet)!
We gave the monkey the banana as it was ...
● ripe (sie)
● teatime (es)
● hungry (er)But there's a lot that MT can't do (yet)!
We gave the monkey the banana as it was ...
● ripe (sie)
● teatime (es)
● hungry (er)
● That's just within the bounds of one sentence!
Hutchins & Somers, 1992:95What else can MT not do (yet)?
● All (non-toy) MT systems translate one
sentence at a time, with no knowledge of
context.
● Clearly this is not how humans process
language, either monolingually or when
performing translation.
● So it's impossible for MT to consistently get
extra-sentential pronominal referents right ...Extra-sentential pronominal reference The soldiers shot at the women. Some of them
Extra-sentential pronominal reference
The soldiers shot at the women. Some of them
● fell (quelques-unes)Extra-sentential pronominal reference
The soldiers shot at the women. Some of them
● fell (quelques-unes)
● missed (quelques-uns)
Adapted from Hutchins & Somers, 1992:96What else can MT not do (yet)?
What happened after the
● Word-sense disambiguation police arrived was a bit
confused. But the result was
The experiment he described pretty clear. You can read it
at the end of the lecture was a in the paper.
bit confused. But the result was
pretty clear. You can read it in
the paper.
Martin Kay's COLING-2014 Invited TalkWhat else can MT not do (yet)?
What happened after the
● Word-sense disambiguation police arrived was a bit
confused. But the result was
The experiment he described pretty clear. You can read it
at the end of the lecture was a in the paper.
bit confused. But the result was
pretty clear. You can read it in
the paper.
Martin Kay's COLING-2014 Invited TalkWhat else can MT not do (yet)?
What happened after the
● Word-sense disambiguation police arrived was a bit
confused. But the result was
The experiment he described pretty clear. You can read it
at the end of the lecture was a in the paper.
bit confused. But the result was
pretty clear. You can read it in
the paper. = article!
Martin Kay's COLING-2014 Invited TalkWhat else can MT not do (yet)?
What happened after the
● Word-sense disambiguation police arrived was a bit
confused. But the result was
The experiment he described pretty clear. You can read it
at the end of the lecture was a in the paper.
bit confused. But the result was = journal!
pretty clear. You can read it in
the paper. = article!
Martin Kay's COLING-2014 Invited TalkHuman Translators can do this easily! “Syntactic vs Pragmatic Translation” (Martin Kay, COLING 2014 Invited Talk) “Est-ce que c'est ta cousine?” “Non, je n'ai pas de cousine.” Is that your cousin? No, I don't have a cousin.
Human Translators can do this easily! “Syntactic vs Pragmatic Translation” (Martin Kay, COLING 2014 Invited Talk) “Est-ce que c'est ta cousine?” “Non, je n'ai pas de cousine.” Is that your cousin? No, I don't have a cousin.
Human Translators can do this easily! “Syntactic vs Pragmatic Translation” (Martin Kay, COLING 2014 Invited Talk) “Est-ce que c'est ta cousine?” “Non, je n'ai pas de cousine.” Is that girl your cousin? No, she's not my cousin.
Human Translators can do this easily!
“Syntactic vs Pragmatic Translation” (Martin
Kay, COLING 2014 Invited Talk)
“Est-ce que c'est ta cousine?” “Non, je n'ai pas de cousine.”
Kay: “More than half
Is that girl your cousin? No, she's not my cousin. the sentences in
Europarl require
pragmatic translation”MT can only do syntactic translation
MT has no 'real world knowledge', i.e. what's in our heads ...
What is the Syntactic Model of Translation?
According to Kay:
• A long translation is a sequence of short(er) translations,
• We memorize short translations (lexical items),
• These short translations can be reordered.
How might we translate “Versichern Sie sich,
dass Sie nichts in dem Zug vergessen haben.”?Make
Versichern
sure
Sie
that
sich
you
dass
not
Sie
anything
nichts
on
in
the
dem
train
Zug
forgotten
vergessen
have
haben
From Martin Kay, COLING 2014Make
Versichern
sure
Sie
that
sich
you
dass
have
Sie
not
nichts
forgotten
in
anything
dem
on
Zug
the
vergessen
train
habenMake
Versichern
sure
Sie
that
sich
you
dass
have
Sie
not
nichts
forgotten
in
anything
dem
on
Zug
the
vergessen
train
haben
This is syntactic translation. None
of you would translate it like this ...What would you really do?
Make
Versichern
sure
Sie
that
sich
you
dass
Sie
nichts
in
dem
Zug
vergessen
habenWhat would you really do?
Make
Versichern
sure
Sie
that
sich
you
dass
Sie
nichts It's OK up to here, but ...
in
dem
Zug
vergessen
habenHow do you do this?
Make
Versichern
sure
Sie
that
sich
you
dass
Sie have
your
nichts
all
in
belongings
dem
Zug
you
with
vergessen
habenWhy depart from syntactic translation?
● To accommodate required target-language
informationWhy depart from syntactic translation?
● To accommodate required target-language
information
● To obtain a smoother resultWhy depart from syntactic translation?
● To accommodate required target-language
information
● To obtain a smoother resultHow else are translators contributing?
● To joint research on Post-editing. Why?
Increased Increased
Implementation Demand for
of MT Post-Editing
● Think-aloud Protocols
● Keyboard Logging
● Eye-Tracking
From Sharon O'Brien, MTM-2014Research Results
● Post-editing can indeed be faster than translation from
scratch, e.g. Guerberof 2012, Flournoy and Duran
(2009), Plitt and Masselot (2010), Autodesk (2011) ...
● However, results vary across engine types, language
pairs, domains, individuals ...
● Even though they were faster at post-editing,
translators will almost consistently report that they
were slower. Does PEMT require a higher cognitive
load than HT?
● 'Good' MT is similar to 80—90% fuzzy matches
From Sharon O'Brien, MTM-2014STAR's 'enhanced fuzzy matching'
STAR's 'enhanced fuzzy matching'
But (some) Translators still
don't like Post-Editing ...
FEAR
PRIDE
STRESS
KNOWLEDGE
SATISFACTION
MONEY
From Sharon O'Brien, MTM-2014Observations
● MT is being used in many ways by many users on a daily basis.
● The point of questioning whether MT is useful or not is moot.
● Many translators find MT to be useful on a daily basis, but that's
all it is – a tool in their armoury – and all it ever will be ...
● No threat to translators' jobs from MT, despite ongoing
scaremongering from some translators.
● MT is not appropriate in all use-cases!
● Different levels of quality are being requested ...
● More influential translators willing to stick their heads above the
parapet and demonstrate the utility of MT.Final hope ...
“Failure to work together in the recent past has
prevented us from making more progress, and the
time is ripe for the two communities [MT
developers and translators] to come together as a
catalyst for further improvements in our translation
systems as we go forward together.”
(Way & Hearne, 2011)away@computing.dcu.ie
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