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Machine Translation:
Approaches, Challenges and Future
Alon Lavie
Language Technologies Institute
Carnegie Mellon University
ITEC Dinner
May 21, 2009
Machine Translation: History
• MT started in 1940’s, one of the first conceived
application of computers
• Promising “toy” demonstrations in the 1950’s, failed
miserably to scale up to “real” systems
• ALPAC Report: MT recognized as an extremely difficult,
“AI-complete” problem in the early 1960’s
• MT Revival started in earnest in 1980s (US, Japan)
• Field dominated by rule-based approaches, requiring
100s of K-years of manual development
• Economic incentive for developing MT systems for small
number of language pairs (mostly European languages)
• Major paradigm shift in MT over the past decade:
– From manually developed rule-based systems
– To Data-driven statistical search-based approaches
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Machine Translation:
Where are we today?
• Age of Internet and Globalization – great demand for
translation services and MT:
– Multiple official languages of UN, EU, Canada, etc.
– Documentation dissemination for large manufacturers (Microsoft,
IBM, Caterpillar)
– Language and translation services business sector estimated at
$16 Billion worldwide in 2008
• Economic incentive is still primarily within a small number of
language pairs
• Some fairly decent commercial products in the market for
these language pairs
– Primarily a product of rule-based systems after many years of
development
– New generation of data-driven “statistical” MT: Google, Language
Weaver
• Web-based (mostly free) MT services: Google, Babelfish,
others…
• Pervasive MT between many language pairs still non-existent,
but Google is trying to change that!
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Representative Example:
Google Translate
• http://translate.google.com
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Google Translate
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Google Translate
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Example of High Quality MT
PAHO’s Spanam system:
•
Mediante petición recibida por la Comisión Interamericana de
Derechos Humanos (en adelante …) el 6 de octubre de 1997, el señor
Lino César Oviedo (en adelante …) denunció que la República del
Paraguay (en adelante …) violó en su perjuicio los derechos a las
garantías judiciales … en su contra.
•
Through petition received by the `Inter-American Commission on
Human Rights` (hereinafter …) on 6 October 1997, Mr. Linen César
Oviedo (hereinafter “the petitioner”) denounced that the Republic of
Paraguay (hereinafter …) violated to his detriment the rights to the
judicial guarantees, to the political participation, to // equal protection
and to the honor and dignity consecrated in articles 8, 23, 24 and 11,
respectively, of the `American Convention on Human Rights`
(hereinafter …”), as a consequence of judgments initiated against it.
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Machine Translation:
Basic Terminology
• Source Language (SL): the language of
the original text that we wish to
translate
• Target Language (TL): the language
into which we wish to translate
• Translation Segment: language “units”
which are translated independently,
usually sentences, sometimes smaller
phrases or terms
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How Does MT Work?
• Naïve MT: Translation Memory
– Store in a database human translations of sentences (or
shorter phrases) that have been already translated before
– When translating a new document:
• For each source sentence, search the DB to see if it has been
translated before
• If found, retrieve it’s translation!
• “Fuzzy matches”, multiple translations
– Main Advantage: translation output is always humanquality!
– Main Disadvantage: many/most sentences haven’t been
translated before, cannot be retrieved…
• Translation Memories are heavily used by the
Commercial Translation Service Provider Industry –
companies such as Echo International
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How Does MT Work?
• All modern MT approaches are based on
building translations for complete
segments by putting together smaller
pieces of translation
• Core Issues:
– What are these smaller pieces of
translation? Where do they come from?
– How does MT put these pieces together?
– How does the MT system pick the correct
(or best) translation among many options?
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Core Challenges of MT
• Ambiguity and Language Divergences:
– Human languages are highly ambiguous, and
differently in different languages
– Ambiguity at all “levels”: lexical, syntactic, semantic,
language-specific constructions and idioms
• Amount of required knowledge:
– Translation equivalencies for vast vocabularies
(several 100k words and phrases)
– Syntactic knowledge (how to map syntax of one
language to another), plus more complex language
divergences (semantic differences, constructions and
idioms, etc.)
– How do you acquire and construct a knowledge base
that big that is (even mostly) correct and consistent?
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Major Sources of Translation
Problems
• Lexical Differences:
– Multiple possible translations for SL word, or
difficulties expressing SL word meaning in a single TL
word
• Structural Differences:
– Syntax of SL is different than syntax of the TL: word
order, sentence and constituent structure
• Differences in Mappings of Syntax to
Semantics:
– Meaning in TL is conveyed using a different syntactic
structure than in the SL
• Idioms and Constructions
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How to Tackle the Core Challenges
• Manual Labor: 1000s of person-years of human
experts developing large word and phrase translation
lexicons and translation rules.
Example: Systran’s RBMT systems.
• Lots of Parallel Data: data-driven approaches for
finding word and phrase correspondences automatically
from large amounts of sentence-aligned parallel texts.
Example: Statistical MT systems.
• Learning Approaches: learn translation rules
automatically from small amounts of human translated
and word-aligned data. Example: AVENUE’s Statistical
XFER approach.
• Simplify the Problem: build systems that are limiteddomain or constrained in other ways. Examples:
CATALYST, NESPOLE!.
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State-of-the-Art in MT
• What users want:
– General purpose (any text)
– High quality (human level)
– Fully automatic (no user intervention)
• We can meet any 2 of these 3 goals
today, but not all three at once:
– FA HQ: Knowledge-Based MT (KBMT)
– FA GP: Corpus-Based (Example-Based) MT
– GP HQ: Human-in-the-loop (efficiency tool)
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Types of MT Applications:
• Assimilation: multiple source languages,
uncontrolled style/topic. General purpose MT,
no semantic analysis. (GP FA or GP HQ)
• Dissemination: one source language,
controlled style, single topic/domain. Special
purpose MT, full semantic analysis. (FA HQ)
• Communication: Lower quality may be okay,
but system robustness, real-time required.
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Approaches to MT: Vaquois MT Triangle
Interlingua
Give-information+personal-data (name=alon_lavie)
Generation
Analysis
Transfer
[s [np [possessive_pronoun “name”]]
[s [vp accusative_pronoun
“chiamare” proper_name]]
[vp “be” proper_name]]
Direct
Mi chiamo Alon Lavie
May 21, 2009
My name is Alon Lavie
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Knowledge-based
Interlingual MT
• The classic “deep” Artificial Intelligence
approach:
– Analyze the source language into a detailed symbolic
representation of its meaning
– Generate this meaning in the target language
• “Interlingua”: one single meaning
representation for all languages
– Nice in theory, but extremely difficult in practice:
• What kind of representation?
• What is the appropriate level of detail to represent?
• How to ensure that the interlingua is in fact universal?
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Interlingua versus Transfer
• With interlingua, need only N parsers/
generators instead of N2 transfer systems:
L2
L2
L1
L3
L1
L3
L4
L6
L4
interlingua
L6
L5
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Multi-Engine MT
• Apply several MT engines to
each input in parallel
• Create a combined
translation from the
individual translations
• Goal is to combine
strengths, and avoid
weaknesses.
• Along all dimensions:
domain limits, quality,
development time/cost,
run-time speed, etc.
• Various approaches to the
problem
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Speech-to-Speech MT
• Speech just makes MT (much) more difficult:
– Spoken language is messier
• False starts, filled pauses, repetitions, out-ofvocabulary words
• Lack of punctuation and explicit sentence boundaries
– Current Speech technology is far from perfect
• Need for speech recognition and synthesis in
foreign languages
• Robustness: MT quality degradation should be
proportional to SR quality
• Tight Integration: rather than separate
sequential tasks, can SR + MT be integrated in
ways that improves end-to-end performance?
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Phrase-based Statistical MT
• Word-to-word and phrase-to-phrase translation pairs
are acquired automatically from data and assigned
probabilities based on a statistical model
• Extracted and trained from very large amounts of
sentence-aligned parallel text
– Word alignment algorithms
– Phrase detection algorithms
– Translation model probability estimation
• Main approach pursued in CMU systems in the
DARPA/TIDES program and now in GALE
– Chinese-to-English and Arabic-to-English
• Most active work is on improved word alignment, phrase
extraction and advanced decoding techniques
• Contact Faculty: Stephan Vogel
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EBMT
• Developed originally for the PANGLOSS system
in the early 1990s
– Translation between English and Spanish
• Generalized EBMT under development for the
past several years
• Used in a variety of projects in recent years
– DARPA TIDES and GALE programs
– DIPLOMAT and TONGUES
• Active research work on improving alignment
and indexing, decoding from a lattice
• Contact Faculty: Ralf Brown and Jaime
Carbonell
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CMU Statistical Transfer (Stat-XFER)
MT Approach
•
•
•
Integrate the major strengths of rule-based and statistical MT within a
common statistically-driven framework:
– Linguistically rich formalism that can express complex and abstract
compositional transfer rules
– Rules can be written by human experts and also acquired automatically
from data
– Easy integration of morphological analyzers and generators
– Word and syntactic-phrase correspondences can be automatically acquired
from parallel text
– Search-based decoding from statistical MT adapted to find the best
translation within the search space: multi-feature scoring, beam-search,
parameter optimization, etc.
– Framework suitable for both resource-rich and resource-poor language
scenarios
Most active work on phrase and rule acquisition from parallel data,
efficient decoding, joint decoding with non-syntactic phrases, MT for
low-resource languages
Contact Faculty: Alon Lavie, Lori Levin, Bob Frederking and Jaime
Carbonell
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Speech-to-Speech MT
• Evolution from JANUS/C-STAR systems to NESPOLE!,
LingWear, BABYLON, TRANSTAC
– Early 1990s: first prototype system that fully performed
sp-to-sp (very limited domains)
– Interlingua-based, but with shallow task-oriented
representations:
“we have single and double rooms available”
[give-information+availability]
(room-type={single, double})
– Semantic Grammars for analysis and generation
– Multiple languages: English, German, French, Italian,
Japanese, Korean, and others
– Phrase-based SMT applied in Speech-to-Speech scenarios
– Most active work on portable speech translation on small
devices: Iraqi-Arabic/English and Thai/English
– Contact Faculty: Alan Black, Stephan Vogel, Florian Metze
and Alex Waibel
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KBMT: KANT, KANTOO, CATALYST
• Deep knowledge-based framework, with symbolic
interlingua as intermediate representation
– Syntactic and semantic analysis into a unambiguous
detailed symbolic representation of meaning using
unification grammars and transformation mappers
– Generation into the target language using unification
grammars and transformation mappers
• First large-scale multi-lingual interlingua-based MT
system deployed commercially:
– CATALYST at Caterpillar: high quality translation of
documentation manuals for heavy equipment
•
•
•
•
Limited domains and controlled English input
Minor amounts of post-editing
Active follow-on projects
Contact Faculty: Eric Nyberg and Teruko Mitamura
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Multi-Engine MT
• New decoding-based approach developed in recent
years under DoD and DARPA funding (used in GALE)
• Main ideas:
– Treat original engines as “black boxes”
– Align the word and phrase correspondences between the
translations
– Build a collection of synthetic combinations based on the
aligned words and phrases
– Score the synthetic combinations based on Language
Model and confidence measures
– Select the top-scoring synthetic combination
• Architecture Issues: integrating “workflows” that
produce multiple translations and then combine them
with MEMT
– IBM’s UIMA architecture
• Contact Faculty: Alon Lavie
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Automatic MT Evaluation
• METEOR: new metric developed at CMU
• Improves upon BLEU metric developed by IBM and used
extensively in recent years
• Main ideas:
– Assess the similarity between a machine-produced
translation and (several) human reference translations
– Similarity is based on word-to-word matching that
matches:
• Identical words
• Morphological variants of same word (stemming)
• synonyms
– Similarity is based on weighted combination of Precision
and Recall
– Address fluency/grammaticality via a direct penalty: how
well-ordered is the matching of the MT output with the
reference?
• Improved levels of correlation with human judgments of
MT Quality
• Contact Faculty: Alon Lavie
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MT at the LTI
• LTI originated as the Center for Machine
Translation (CMT) in 1985
• MT continues to be a prominent sub-discipline
of research with the LTI
– More MT faculty than any of the other areas
– More MT faculty than anywhere else
• Active research on all main approaches to MT:
Interlingua, Transfer, EBMT, SMT
• Leader in the area of speech-to-speech MT
• Multi-Engine MT (MEMT)
• MT Evaluation (METEOR)
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Summary
• Main challenges for current state-of-the-art MT
approaches - Coverage and Accuracy:
– Acquiring broad-coverage high-accuracy translation
lexicons (for words and phrases)
– learning syntactic mappings between languages from
parallel word-aligned data
– overcoming syntax-to-semantics differences and
dealing with constructions
– Stronger Target Language Modeling
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Questions…
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Lexical Differences
• SL word has several different meanings, that
translate differently into TL
– Ex: financial bank vs. river bank
• Lexical Gaps: SL word reflects a unique
meaning that cannot be expressed by a single
word in TL
– Ex: English snub doesn’t have a corresponding verb
in French or German
• TL has finer distinctions than SL  SL word
should be translated differently in different
contexts
– Ex: English wall can be German wand (internal),
mauer (external)
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Structural Differences
• Syntax of SL is different than syntax of the
TL:
– Word order within constituents:
• English NPs: art adj n
• Hebrew NPs: art n art adj
– Constituent structure:
the big boy
ha yeled ha gadol
• English is SVO: Subj Verb Obj I saw the man
• Modern Arabic is VSO: Verb Subj Obj
– Different verb syntax:
• Verb complexes in English vs. in German
I can eat the apple Ich kann den apfel essen
– Case marking and free constituent order
• German and other languages that mark case:
den apfel esse Ich the(acc) apple eat I(nom)
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Syntax-to-Semantics Differences
• Meaning in TL is conveyed using a different
syntactic structure than in the SL
–
–
–
–
Changes in verb and its arguments
Passive constructions
Motion verbs and state verbs
Case creation and case absorption
• Main Distinction from Structural Differences:
– Structural differences are mostly independent of
lexical choices and their semantic meaning  can be
addressed by transfer rules that are syntactic in
nature
– Syntax-to-semantic mapping differences are
meaning-specific: require the presence of specific
words (and meanings) in the SL
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Syntax-to-Semantics Differences
• Structure-change example:
I like swimming
“Ich scwhimme gern”
I swim gladly
• Verb-argument example:
Jones likes the film.
“Le film plait à Jones.”
(lit: “the film pleases to Jones”)
• Passive Constructions
– Example: French reflexive passives:
Ces livres se lisent facilement
*”These books read themselves easily”
These books are easily read
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Idioms and Constructions
• Main Distinction: meaning of whole is
not directly compositional from meaning
of its sub-parts  no compositional
translation
• Examples:
– George is a bull in a china shop
– He kicked the bucket
– Can you please open the window?
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Formulaic Utterances
• Good night.
• tisbaH
cala xEr
waking up on good
• Romanization of Arabic from CallHome Egypt
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Analysis and Generation
Main Steps
• Analysis:
– Morphological analysis (word-level) and POS tagging
– Syntactic analysis and disambiguation (produce
syntactic parse-tree)
– Semantic analysis and disambiguation (produce
symbolic frames or logical form representation)
– Map to language-independent Interlingua
• Generation:
– Generate semantic representation in TL
– Sentence Planning: generate syntactic structure and
lexical selections for concepts
– Surface-form realization: generate correct forms of
words
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Direct Approaches
• No intermediate stage in the translation
• First MT systems developed in the
1950’s-60’s (assembly code programs)
– Morphology, bi-lingual dictionary lookup,
local reordering rules
– “Word-for-word, with some local word-order
adjustments”
• Modern Approaches:
– Phrase-based Statistical MT (SMT)
– Example-based MT (EBMT)
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Statistical MT (SMT)
• Proposed by IBM in early 1990s: a direct,
purely statistical, model for MT
• Most dominant approach in current MT
research
• Evolved from word-level translation to phrasebased translation
• Main Ideas:
– Training: statistical “models” of word and phrase
translation equivalence are learned automatically
from bilingual parallel sentences, creating a bilingual
“database” of translations
– Decoding: new sentences are translated by a
program (the decoder), which matches the source
words and phrases with the database of translations,
and searches the “space” of all possible translation
combinations.
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Statistical MT (SMT)
• Main steps in training phrase-based statistical MT:
– Create a sentence-aligned parallel corpus
– Word Alignment: train word-level alignment models
(GIZA++)
– Phrase Extraction: extract phrase-to-phrase translation
correspondences using heuristics (Pharoah)
– Minimum Error Rate Training (MERT): optimize translation
system parameters on development data to achieve best
translation performance
• Attractive: completely automatic, no manual rules,
much reduced manual labor
• Main drawbacks:
– Translation accuracy levels vary
– Effective only with large volumes (several mega-words) of
parallel text
– Broad domain, but domain-sensitive
– Still viable only for small number of language pairs!
• Impressive progress in last 5 years
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EBMT Paradigm
New Sentence (Source)
Yesterday, 200 delegates met with President Clinton.
Matches to Source Found
Yesterday, 200
delegates met behind
closed doors…
Gestern trafen sich 200
Abgeordnete hinter
verschlossenen…
Difficulties with
President Clinton…
Schwierigkeiten mit
Praesident Clinton…
Alignment (Sub-sentential)
Yesterday, 200 delegates
met behind closed
doors…
Gestern trafen sich 200
Abgeordnete hinter
verschlossenen…
Difficulties with
President Clinton over…
Schwierigkeiten mit
Praesident Clinton…
Translated Sentence (Target)
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Gestern
trafen sich 200 Abgeordnete mit Praesident
Transfer Approaches
• Syntactic Transfer:
– Analyze SL input sentence to its syntactic structure
(parse tree)
– Transfer SL parse-tree to TL parse-tree (various
formalisms for specifying mappings)
– Generate TL sentence from the TL parse-tree
• Semantic Transfer:
– Analyze SL input to a language-specific semantic
representation (i.e., Case Frames, Logical Form)
– Transfer SL semantic representation to TL semantic
representation
– Generate syntactic structure and then surface
sentence in the TL
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Transfer Approaches
Main Advantages and Disadvantages:
• Syntactic Transfer:
– No need for semantic analysis and generation
– Syntactic structures are general, not domain specific
 Less domain dependent, can handle open domains
– Requires word translation lexicon
• Semantic Transfer:
– Requires deeper analysis and generation, symbolic
representation of concepts and predicates  difficult to
construct for open or unlimited domains
– Can better handle non-compositional meaning structures
 can be more accurate
– No word translation lexicon – generate in TL from symbolic
concepts
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The METEOR Metric
• Example:
– Reference: “the Iraqi weapons are to be handed over to the
army within two weeks”
– MT output: “in two weeks Iraq’s weapons will give army”
• Matching: Ref: Iraqi weapons army two weeks
MT: two weeks Iraq’s weapons army
•
•
•
•
•
P = 5/8 =0.625 R = 5/14 = 0.357
Fmean = 10*P*R/(9P+R) = 0.3731
Fragmentation: 3 frags of 5 words = (3-1)/(5-1) = 0.50
Discounting factor: DF = 0.5 * (frag**3) = 0.0625
Final score: Fmean * (1- DF) = 0.3731*0.9375 = 0.3498
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Synthetic Combination MEMT
Two Stage Approach:
1. Align: Identify common words and phrases across
the translations provided by the engines
2. Decode: search the space of synthetic combinations
of words/phrases and select the highest scoring
combined translation
Example:
1.
2.
announced afghan authorities on saturday reconstituted
four intergovernmental committees
The Afghan authorities on Saturday the formation of the
four committees of government
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Synthetic Combination MEMT
Two Stage Approach:
1. Align: Identify common words and phrases across
the translations provided by the engines
2. Decode: search the space of synthetic combinations
of words/phrases and select the highest scoring
combined translation
Example:
1.
2.
announced afghan authorities on saturday reconstituted
four intergovernmental committees
The Afghan authorities on Saturday the formation of the
four committees of government
MEMT: the afghan authorities announced on Saturday the
formation of four intergovernmental committees
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