1. Machine Translation Overview
Alon Lavie
Language Technologies Institute
Carnegie Mellon University
LTI Open House
February 23, 2012

2. Machine Translation: History
1946: MT is one of the first conceived applications of modern computers (A.D. Booth, Alan Turing)
1954: The “Georgetown Experiment” Promising “toy” demonstrations of Russian-English MT
Late 1950s and early 1960s: MT fails to scale up to “real” systems
1966: ALPAC Report: MT recognized as an extremely difficult, “AI-complete” problem. Funding disappears
1968: SYSTRAN founded
1985: CMU “Center for Machine Translation” (CMT) founded
Late 1980s and early 1990s: Field dominated by rule-based approaches – KBMT, KANT, Eurotra, etc.
1992: “Noisy Channel” Statistical MT models invented by IBM researchers (Brown, Della Pietra, et al.). CANDIDE
Mid 1990s: First major DARPA MT Program. PANGLOSS
Late 1990s: Major Speech-to-Speech MT demonstrations: C-STAR
1999: JHU Summer Workshop results in GIZA
2000s: Large DARPA Funding Programs – TIDES and GALE
2003: Och et al introduce Phrase-based SMT. PHARAOH
2006: Google Translate is launched
2007: Koehn et al release MOSES
February 23, 2012
LTI Open House 2012

3. 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.
Software Localization and documentation dissemination for large manufacturers (Microsoft, Intel, Apple, EBay, ALCOA, etc.)
Language and translation services business sector estimated at $26 Billion worldwide in 2010 and growing at a healthy pace
Volume of online content growing exponentially
Economic incentive is still primarily within a small number of language pairs
Some fairly decent commercial products in the market for these language pairs
Product of rule-based systems after many years of development: SYSTRAN, PROMT, others…
New generation of data-driven “statistical” MT systems: SDL/Language Weaver, Asia Online, Safaba, others…
Web-based (mostly free) MT services: Google, MS-Bing, Babelfish, others…
Pervasive MT between many language pairs still non-existent, but some significant progress in recent years
February 23, 2012
LTI Open House 2012

4. How Does MT Work?
All modern MT approaches are based on building translations for complete sentences by putting together smaller pieces of translation
Core Questions:
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?
February 23, 2012
LTI Open House 2012

5. 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?
February 23, 2012
LTI Open House 2012

6. 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: PROMT’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: Google’s Statistical MT systems.
Learning Approaches: learn translation rules automatically from linguistically annotated human translated and word-aligned data. Example: Syntax-based SMT approaches.
Simplify the Problem: build systems that are limited-domain or constrained in other ways. Example: CMU’s limited-domain speech translation.
February 23, 2012
LTI Open House 2012

7. Rule-based vs. Data-driven Approaches to MT
What are the pieces of translation? Where do they come from?
Rule-based: large-scale “clean” word translation lexicons, manually constructed over time by experts
Data-driven: broad-coverage word and multi-word translation lexicons, learned automatically from available sentence-parallel corpora
How does MT put these pieces together?
Rule-based: large collections of rules, manually developed over time by human experts, that map structures from the source to the target language
Data-driven: a computer algorithm that explores millions of possible ways of putting the small pieces together, looking for the translation that statistically looks best
February 23, 2012
LTI Open House 2012

8. Rule-based vs. Data-driven Approaches to MT
How does the MT system pick the correct (or best) translation among many options?
Rule-based: Human experts encode preferences among the rules designed to prefer creation of better translations
Data-driven: a variety of fitness and preference scores, many of which can be learned from available training data, are used to model a total score for each of the millions of possible translation candidates; algorithm then selects and outputs the best scoring translation
February 23, 2012
LTI Open House 2012

9. Rule-based vs. Data-driven Approaches to MT
Why have the data-driven approaches become so popular?
We can now do this!
Increasing amounts of sentence-parallel data are constantly being created on the web
Advances in machine learning algorithms
Computational power of today’s computers can train systems on these massive amounts of data and can perform these massive search-based translation computations when translating new texts
Building and maintaining rule-based systems is too difficult, expensive and time-consuming
In many language and data scenarios, it actually works better!
February 23, 2012
LTI Open House 2012

10. Statistical MT (SMT)
Data-driven, most dominant approach in current MT research
Originally proposed by IBM in early 1990s: a direct, purely statistical, model for MT
Evolved from word-level translation to phrase-based translation
Main Ideas:
Training: statistical “models” of word and phrase translation equivalence are learned automatically from bilingual parallel sentences, creating a bilingual “database” of sub-sentential translation candidates
Decoding: new sentences are translated by a computer program (the decoder), which matches the source words and phrases with the database of translations, and searches the “space” of all possible translation combinations.
February 23, 2012
LTI Open House 2012

11. Statistical MT: Major Challenges
Current approaches are too naïve and “direct”:
Good at learning word-to-word and phrase-to-phrase correspondences from data
Not good enough at learning how to combine these pieces and reorder them properly during translation
Learning general rules requires much more complicated algorithms and computer processing of the data
The space of translations that is “searched” often doesn’t contain a perfect translation
The fitness scores that are used aren’t good enough to always assign better scores to the better translations  we don’t always find the best translation even when it’s there!
Optimization is brittle, problematic and metric-dependent!
Solutions:
Google solution: more and more data!
Research solution: “smarter” algorithms and learning methods
February 23, 2012
LTI Open House 2012

12. Rule-based vs. Data-driven MT
We thank all participants of the whole world for their comical and creative drawings; to choose the victors was not easy task!
Click here to see work of winning European of these two months, and use it to look at what the winning of USA sent us.
We thank all the participants from around the world for their designs cocasses and creative; selecting winners was not easy!
Click here to see the artwork of winners European of these two months, and disclosure to look at what the winners of the US have been sending.
Rule-based
Data-driven
February 23, 2012
LTI Open House 2012

13. Representative Example: Google Translate
February 23, 2012
LTI Open House 2012

14. Google Translate
February 23, 2012
LTI Open House 2012

15. Google Translate
February 23, 2012
LTI Open House 2012

16. Types of MT Applications:
Assimilation: multiple source languages  one target language, uncontrolled style/topic.
Dissemination: one source language  multiple target languages, often controlled style, single topic/domain.
Communication: Speech, chat, text messages. Real-time is critical. Lower quality may be okay due to collaborative setting.
February 23, 2012
LTI Open House 2012

17. Approaches to MT: Vaquois MT Triangle
Interlingua
Give-information+personal-data (name=alon_lavie)
Generation
Analysis
Transfer
[s [vp accusative_pronoun “chiamare” proper_name]]
[s [np [possessive_pronoun “name”]]
[vp “be” proper_name]]
Direct
Mi chiamo Alon Lavie
My name is Alon Lavie
February 23, 2012
LTI Open House 2012

18. 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
February 23, 2012
LTI Open House 2012

19. Speech-to-Speech MT Speech just makes MT (much) more difficult:
Spoken language is messier
False starts, filled pauses, repetitions, out-of-vocabulary 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?
February 23, 2012
LTI Open House 2012

20. 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
Active research on all main approaches to MT
Leader in the area of speech-to-speech MT
Multi-Engine MT (MEMT)
MT Evaluation (METEOR)
Spin-off Companies:
Jibbigo (speech translation on mobile devices)
Safaba (MT solutions for enterprises and LSPs)
February 23, 2012
LTI Open House 2012

21. MT Faculty at LTI Alon Lavie Ralf Brown Jaime Carbonell Lori Levin
Noah Smith
Alan Black
Bob Frederking
Florian Metze
Alex Waibel
Teruko Mitamura
Eric Nyberg
February 23, 2012
LTI Open House 2012

22. 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 structural mappings between languages from parallel word-aligned data
overcoming syntax-to-semantics differences and dealing with constructions
Effective Target Language Modeling
Context-dependent modeling and adaptation
Novel algorithms for model acquisition and decoding
February 23, 2012
LTI Open House 2012

23. Questions…
February 23, 2012
LTI Open House 2012

24. 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
February 23, 2012
LTI Open House 2012

25. 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 (Post-editing)
February 23, 2012
LTI Open House 2012

26. 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?
“Demonstration” is now set of KANT interlinguas
Downloaded here from AMTA interlingua workshop webpage: clicking on sentences show the IL
Note use of phrasal lexical units!
Start with #4: “simple” example (with a 2-level genl. quantifier!)
1: rel. clause
3: conj “but”, coordination, another genl. quant.
5: implicit rel. clause, apposition
6: “latter”/”former”
7: appositive VP
8: quote marks
13/14: complex [numbers different on frontpage and files!]
21/22: “all” is (:OR SINGULAR PLURAL) [ambiguity packing!]
February 23, 2012
LTI Open House 2012

27. Interlingua versus Transfer
With interlingua, need only N parsers/ generators instead of N2 transfer systems: L2 L2 L3 L1 L3 L1
interlingua L6 L4 L6 L4 L5 L5
February 23, 2012
LTI Open House 2012

28. 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
February 23, 2012
LTI Open House 2012

29. CMU Statistical Transfer (Stat-XFER) MT Approach
Integrate the major strengths of rule-based and statistical MT within a common syntax-based 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 syntax-driven 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, effective syntactic modeling, MT for low-resource languages
Contact Faculty: Alon Lavie, Lori Levin, Bob Frederking and Jaime Carbonell
February 23, 2012
LTI Open House 2012

30. 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
February 23, 2012
LTI Open House 2012

31. 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
February 23, 2012
LTI Open House 2012

32. 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
Some active follow-on projects
Contact Faculty: Eric Nyberg and Teruko Mitamura
February 23, 2012
LTI Open House 2012

33. Multi-Engine MT
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 a variety of features, including n-gram support and Language Model
Parameter Tuning: Learn optimal weights using MERT
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
February 23, 2012
LTI Open House 2012

34. Automated MT Evaluation
METEOR: Automated 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 and paraphrases
Address fluency/grammaticality via a direct penalty: how well-ordered is the matching of the MT output with the reference?
Tunable Weights: Weights for Precision, Recall, Fragmentation are tuned for optimal correlation with human judgments
Outcome: Much improved levels of correlation!
Contact Faculty: Alon Lavie
February 23, 2012
LTI Open House 2012

35. Safaba Translation Solutions
Recent CMU commercial spin-off company in the MT area
Mission: Develop and deliver advanced translation automation software solutions for the commercial translation business sector
Target Clients: Language Services Providers (LSPs) and their enterprise clients
Primary Service:
Software-as-a-Service customized MT Technology: Develop specialized highly-scalable software for delivering high-quality client-customized Machine Translation (MT) based on a low-cost SaaS model
Other Related Services:
Consulting Services: Analyze LSP/client translation processes and technologies and advise clients on effective solutions for increasing their translation automation
Software Implementation Services: Design and implement custom translation automation solutions for LSPs and/or enterprise clients
Contact Faculty: Alon Lavie
February 23, 2012
LTI Open House 2012

36. 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)
February 23, 2012
LTI Open House 2012

37. Structural Differences
Syntax of SL is different than syntax of the TL:
Word order within constituents:
English NPs: art adj n the big boy
Hebrew NPs: art n art adj ha yeled ha gadol
Constituent structure:
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)
February 23, 2012
LTI Open House 2012

38. 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
February 23, 2012
LTI Open House 2012

39. 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
February 23, 2012
LTI Open House 2012

40. 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?
February 23, 2012
LTI Open House 2012

41. Formulaic Utterances Good night. tisbaH cala xEr waking up on good
Romanization of Arabic from CallHome Egypt
February 23, 2012
LTI Open House 2012

42. Analysis and Generation Main Steps
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
February 23, 2012
LTI Open House 2012

43. 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)
February 23, 2012
LTI Open House 2012

44. 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 phrase-based 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.
February 23, 2012
LTI Open House 2012

45. 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
February 23, 2012
LTI Open House 2012

46. Matches to Source Found
EBMT Paradigm
New Sentence (Source)
Yesterday, 200 delegates met with President Clinton.
Matches to Source Found
Yesterday, 200 delegates met behind closed doors…
Difficulties with President Clinton…
Gestern trafen sich 200 Abgeordnete hinter verschlossenen…
Schwierigkeiten mit Praesident Clinton…
Alignment (Sub-sentential)
Yesterday, 200 delegates met behind closed doors…
Difficulties with President Clinton over…
Gestern trafen sich 200 Abgeordnete hinter verschlossenen…
Schwierigkeiten mit Praesident Clinton…
Translated Sentence (Target)
February 23, 2012
LTI Open House 2012
Gestern trafen sich 200 Abgeordnete mit Praesident Clinton.

47. Transfer Approaches Syntactic Transfer: Semantic 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
February 23, 2012
LTI Open House 2012

48. 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
February 23, 2012
LTI Open House 2012

49. The METEOR Metric Example: P = 5/8 =0.625 R = 5/14 = 0.357
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 = R = 5/14 = 0.357
Fmean = 10*P*R/(9P+R) =
Fragmentation: 3 frags of 5 words = (3-1)/(5-1) = 0.50
Discounting factor: DF = 0.5 * (frag**3) =
Final score: Fmean * (1- DF) = * =
February 23, 2012
LTI Open House 2012

50. Synthetic Combination MEMT
Two Stage Approach:
Align: Identify common words and phrases across the translations provided by the engines
Decode: search the space of synthetic combinations of words/phrases and select the highest scoring combined translation
Example:
announced afghan authorities on saturday reconstituted four intergovernmental committees
The Afghan authorities on Saturday the formation of the four committees of government
February 23, 2012
LTI Open House 2012

51. Synthetic Combination MEMT
Two Stage Approach:
Align: Identify common words and phrases across the translations provided by the engines
Decode: search the space of synthetic combinations of words/phrases and select the highest scoring combined translation
Example:
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
February 23, 2012
LTI Open House 2012

52. 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 (Moses)
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 widely
Effective only with large volumes (several mega-words) of parallel text
Broad domain, but domain-sensitive
Viable only for limited number of language pairs!
Impressive progress in last 5-10 years!
February 23, 2012
LTI Open House 2012