the tipping point
TRANSCRIPT
The Tipping PointAndrzej Zydroń CTO XTM Intl
Localization World 2014 Vancouver
The Tipping Point
OCR analogy:
• 1978 Kurzweil Computer Products launches OCR
• Initial quality varied average up to 90%
- Still quicker and cheaper to retype and proof
• Gradual improvements including extensive use of dictionaries
- 1990 quality up to 97%
• 1990’s
- Better algorithms, faster processors, cheaper RAM, extensive use of dictionaries, dynamic training, multiple script support
• 2000 – quality up to 99%
The Tipping Point
Language
Global Demand
12% pa growth
Average Price Paradox
Average Price Paradox
• Automation• More competition• More resources• Better technology• Machine translation
The Translation Puzzle
The Translation Puzzle
The Translation Puzzle
Project Manager requirements:
– Real-time projects
• Creation
• Tracking
• Communication
– Translation assets – TM, Terminology
– Financial management
The Translation Puzzle
Client / Requestor requirements:
– Project creation
– Cost confirmation
– Project tracking
– Quality review
– Translation pick up
The Translation Puzzle
Linguist requirements:
– Work effectively as a team
– Access to the most up to date assets
– Ensure translation quality
– WYSIWYG preview of target files
– Meet deadlines
Putting the Pieces Together
Swift collaboration of all the project contributors with real-time data
sharing and tracking.
Machine Translation
In a nutshell:
– 1950’s IBM/Washington University/Georgetown University
• Transfer systems
• ALPAC Report – 1966
– More expensive, slower, less accurate
– Ambiguity/Complexity of language
– Context
– 1970’s/1980’s
• Systran (USAF, Xerox, Caterpillar, European Commission), Canadian Meteo
– Statistical Machine Translation (SMT) 2000’s
• EU funded research: Moses
• Statistical/Example based translation (Och, Ney, Koehn, Marcu)
– Big Data: 1million+ aligned sentences
SMT
A great success:
– Google Translate
– Microsoft Translator
– Asia Online
– Safaba
– Tauyou
– DoMY
– Etc.
SMT
Cannot overemphasise the contribution:
– European Union
– Academic institutions:
• Edinburg University
• Carnegie Mellon
• Princeton University
• John Hopkins University
• University of Pennsylvania
• CNGL
– Dublin City University
– Trinity College
– University of Limerick
SMT
In a nutshell:
– Based on: Information Theory
• Bayesian theory:
• Translation model
– Probability that the source string is the translation of the target string
– Given enough data we can calculate the probability that word ‘A’ is translation for word ‘X’
SMT
Limitations:
– You need an awful lot of data
– Probabilities are at best a ‘guess’
– Word order issues,
• English and German
• English Japanese
– Morphology difficulties
• Impoverished to rich, e.g. English to Polish
– Terminology
– Workflow
– Real time retraining
SMT
Limitations:
– Currently these are an impediment to further SMT adoption
FALCON:
– EU FP7 funded project
– Federated Active Linguistic data CuratiON
– Members
• Dublin City University
• Trinity College Dublin
• Easyling
• Interverbum
• XTM International
– Currently half way into 2 year project
– Tight integration
• Easyling
• TermWeb
• XTM
– L3Data
• Linked Language and Localisation Data
• SPARQL linking and curation of language resources
– Advances in SMT
• Adding Babelnet – Lexical Big Data
• Dynamic retraining
• Optimal segment translation sequence
• Forcing terminology (forced decoding)
• Workflow integration
• L3Data curation and sharing
Lays a golden egg
Babelnet:http://www.babelnet.org
• Lexical Big Data
• Sapienza Università di Roma
– Roberto Navilgi
– ERC funded project
• Princeton WordNet
• Wikipedia
• Wiktionary
• DBPedia
• 9.5 million entries
• Equivalents in 50 languages
Moses + Babelnet:
Moses: SMT Big Data
Babelnet: Lexical Big Data
Babelnet + Moses =
much improved SMT
Babelnet + Segment Alignment =
much improved alignment
Dynamic retraining:
– New feature
– Moses learns on the fly as translation/post editing happened
– Immediate benefits from translator output
Optimal translation sequence:Prioritize translation for dynamic retraining
Forced decoding:
– Terminology system integration
– Prompt the Moses decoder to use a specific term
– Immediate benefits for translator
das ist ein kleines <term
translation="dwelling”>Haus</term>
Workflow integration:
– Making SMT part of an integrated TMS workflow
• Terminology: forced decoding
• Babelnet input
• Translation Memory
• Browser based Translator Workbench
• Dynamic retraining
• Optimal sequence
• Always up to date SMT engines
Workflow integration:
L3Data curation and sharing:
Publish
Correct & refine
Lex-concept lifecycle
Correct & refine
Discover & use
Discover & use
Correct & refine
Bitext lifecycle
Discover data
(Re)train-MT
Revise and annotate
Publish
Content lifecycle
Publish
I18n & source QA
Trans QA
Post-edit
Automated translation
Consume Create
Limits of current technology
– We are making significant progress
• Big Data generated dictionaries
– 9.5 million+ entries
• Phrase based alignment/translation
• Syntax based translation
• Hierarchical phrase based translation
– Marker/Function words
Limits of current technology
– There are limits with current technology
• Syntax
• Morphology
• Grammar
• Statistical anomalies
• Data dilution
• Idioms
• Out of Vocabulary words
• Morphology
– Computers can never ‘understand’ the text
– Next generation systems need a completely approach
John Searle’s Chinese Room
Defining Intelligence
Human vs Computer• Human 200 OPS
• Computer 82,300,000,000 OPS
vs
How the brain works
30 billion cells, 100 trillion synapses
How the brain works
How the brain works
• Trajectory• Velocity• Angle• Wind speed • Direction
How the brain works
How the brain works
How the brain works
Human Intelligence
Jeff Hawkins: On Intelligence 2004 ISBN 0-8050-7456-2
• Understanding cannot be measured by external behavior
• Understanding is an internal metric of how the brain remembers things to
make predictions
• AI programs do not simulate brains and are not intelligent
• All intelligence is concentrated in the neocortex and the synapses that connect
different parts of the brain
• Intelligence is primarily based on hierarchical pattern matching starting with
an ‘invariant form’: house, animal, dog
• All animals exploit patterns in nature
Simulating Human Intelligence
Beyond Turing
Biological intelligence
Neocortical architecture
Numenta
Cortical theory
Sparse distributed architecture
Pattern matching
Hierarchical Temporal Memory
Simulating Human Intelligence
Hierarchical Temporal Sequence Memory:
Regions
• Learn sequences of common spacial patterns
• Pass stable representations up hierarchy
• Unfold sequences going down hierarchy
Hierarchy
• Reduces memory and training time
• Provides means of generalization
Question and Answer session
Better Translation Technology
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