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TAUS recently performed an extensive study of the machine translation market and estimates its size at $250 million. Despite the relatively small size of the market it has outsize impact and innovation power. TAUS believes that MT technology is a key enabler and a force multiplier for new services and growth. In our report we cover different uses of MT technology, survey players and offerings in the market and analyze the impact of large online MT providers and open source technology. We identified 65 MT operators and break down the market by offering type and region. We analyzed market trends, opportunities and challenges as well as market drivers and inhibitors. Based on our data and observations we venture a few predictions for the MT market. This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit. MosesCore is supported by the European Commission Grant Number 288487 under the 7th Framework Programme. For the latest updates go to http://www.statmt.org/mosescore/ or follow us on Twitter - #MosesCore
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TAUS MACHINE TRANSLATION SHOWCASE Vancouver, Canada
TAUS Introduction and MT Market Overview Wednesday, 29 October 2014 Jaap van der Meer & Achim Ruopp, TAUS
The research within the project MosesCore leading to these results has received funding from the European Union 7th Framework Programme, grant agreement no 288487
TAUS Introduction and MT Market Overview
Jaap van der Meer, TAUS Achim Ruopp, TAUS Localiza)on World Vancouver 29-‐Oct-‐2014
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TAUS Machine TranslaHon Showcase 13:30 / TAUS Introduc7on and MT market overview, Achim Ruopp (TAUS) 14:00 / Machine Transla7on at eBay, Saša Hassan (eBay) 14:30 / The Simplified Guide to GeGng Started in SMT, Tom Hoar (Precision
Transla)on Tools) 15:00 / Coffee Break 15:30 / Seamless Globaliza7on with distributed crowd post edi7ng, Vasco
Pedro (Unbabel) 16:00 / Introduc7on to Matecat, the open-‐source CAT tool for post-‐edi7ng,
Marco TrombeM (Translated) 16:30 / Podium Discussion 17:00 / Adjourn MosesCore is supported by the European Commission Grant Number 288487
under the 7th Framework Programme.
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The Changing Nature of the MT Market o ExecuHve Summary and Mega Trends o Past, Present and Future of MT Research o Different Usages of Machine TranslaHon o Types of Players o Types of Offerings o Defining the Market – the Numbers o Market OpportuniHes and Challenges o Market Drivers and Inhibitors o PredicHons
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TAUS Machine TranslaHon Market Report Execu)ve Summary o Market size: $250 Million, growing 16.9% per year o “Perfect storm condiHons” for MT o Key trends:
§ GlobalizaHon, IntegraHon, Convergence, VerHcalizaHon, Immediacy of communicaHon, Privacy – security, High quality translaHon
o OpportuniHes: § Business expansion, IntegraHon of MT, ProducHvity gains, MT as enabler for new
services, Narrow domain applicaHons, Customer support self-‐service o Challenges:
§ False expectaHons – false starts, Quality of MT, Language coverage, Available training data, Specialist skills, Vendor lock-‐in, CompeHHon from free MT, Quality measurement & esHmaHon
o PredicHons: § Post-‐ediHng MT will grow very quickly and become the primary producHon process in
translaHon within five years. § MT technology itself is on its way to become a commodity, shijing the Holy Grail to
the data
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“Perfect Storm CondiHons”
1. Ease of communications
2. Hyperglobalization
3. Democratization of knowledge
4. Linguistic diversity
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Entering the Convergence Era
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Past, Present and Future of MT Research History of Machine TranslaHon Research
o Many ups and downs since the 1950s o Strong compeHHon between vastly different approaches o Sudden leaps of improvement o Ojen parallel development in academia, government and industry
o Moved from ridicule to acceptance for many uses over the last couple of years § Cynic’s view that FAHQMT “fully automated high quality machine
translaHon” is always five years away misses the point
o Lately academic research shared as open source
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Past, Present and Future of MT Research Current Trends -‐ Hybrid and Other Approaches o Combine the best features of the linguisHc approach and the more modern staHsHcal approach § Ojen leads to higher output quality § Lower customizaHon costs
o Leads to bewildering range of opHons for building the best MT system for a specific language pair and use case § Common pracHce of picking single/few opHons has been likened to “alchemy” by leading MT researcher
o Further adopHon of modern AI techniques § Deep learning with neural networks is hot research topic
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Different Usages of Machine TranslaHon GisHng (AssimilaHon)
o Understanding the gist or central point of a text or conversaHon in a foreign language
o Conveying the semanHc meaning more important than syntacHc/grammaHcal correctness
o Highest volume use of machine translaHon currently o Examples
§ “Translate this page” links in Google search results § “Translate” links for Facebook posts § Hotel reviews on TripAdvisor § Augmented reality sign translaHons in Wordlens app
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Different Usages of Machine TranslaHon Search and Discovery
o Discovery of foreign language content of relevance to the searcher § Previously ojen not discoverable
o Closely related to gisHng o Huge opportunity for human translaHon
§ Follow-‐up human translaHon of discovered content
o Examples § eDiscovery – finding relevant documents for legal cases § Patent translaHon § News translaHon/monitoring
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Different Usages of Machine TranslaHon SenHment Analysis
o AutomaHc detecHon of senHment, ojen negaHve or posiHve senHment, in foreign language content
o Basically: discovery and gisHng for machines o Difficult in mulHlingual content as ojen two imprecise staHsHcal systems are involved § Machine translaHon § SenHment analyzer
o Example § Stock trading based on senHment analysis
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Different Usages of Machine TranslaHon Post-‐ediHng (DisseminaHon)
o Human ediHng of machine translated content to a desired quality level o Quickly becoming part of the tool set in the translaHon industry o Various studies: 30-‐40% producHvity gain o More important: Faster turn-‐around Hmes o Useful for many, but not all translaHon jobs o AdopHon challenges
§ IntegraHon into workflows § Difficult customizaHon and evaluaHon § Translator concerns
o Research into Hghter MT – ediHng integraHon to aid editor in best possible way
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Different Usages of Machine TranslaHon Speech TranslaHon
o Speech-‐to-‐speech translaHon requires combinaHon of three systems § AutomaHc Speech RecogniHon (ASR) § Machine TranslaHon § Text-‐to-‐Speech (TTS)
o CombinaHon of three staHsHcal systems! o Spoken language more difficult to machine translate than well formed text
o Despite difficulty many system/apps in this intriguing area § Promises immersion into foreign language environment
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Types of Players in the Machine TranslaHon Market o MT suppliers
§ Long established players o Ojen started out with strong economic basis of government/insHtuHonal buyer
§ New players o Using opportunity of increasing MT awareness/adopHon o Ojen using available open source soluHons as a basis
§ Has commodizaHon started?
o Value-‐added resellers § Using machine translaHon to enhance/complement an exisHng service § See different uses of machine translaHon § More unexpected innovaHve uses expected § Most important value proposiHon of MT?
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Types of Players in the Machine TranslaHon Market
o Free online machine translaHon services § Google/Microsoj/Yandex/Baidu § Cross-‐subsidized by uses that generate revenue e.g. adverHsing, platorm use
§ Paid API use o In-‐house users of machine translaHon
§ Governments, mulHnaHonal organizaHons and mulHnaHonal companies
§ Strategic importance warrants costs of developing/maintaining MT systems in-‐house
§ Most flexibility
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Types of Offerings in the Machine TranslaHon Market Licenses and MTaaS
o Licenses § TradiHonal model of sojware distribuHon
o SHll important for server, not desktop § Provides a lot of flexibility and opHons for customizaHon
o OperaHonal know-‐how required § Provides highest degree of privacy § Allows translaHon of unlimited number of words
o Machine TranslaHon as a Service (MTaaS) § MT running on MT provider infrastructure § Ojen with subscripHon pricing
o In many cases preferable for supplier and buyer over high up-‐front licensing fees
§ Web-‐based user interfaces for MT training/operaHon o Some loss of flexibility/control o Presets not always a negaHve
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Types of Offerings in the Machine TranslaHon Market Volume-‐Based Machine TranslaHon Services
o Online machine translaHon services aim to provide machine translaHon § In many language pairs § Worldwide via the internet
o General domain § CustomizaHon only via Microsoj Translator Hub
o Very affordable o Cross-‐subsidizaHon puts long-‐term availability of services into quesHon
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Types of Offerings in the Machine TranslaHon Market Professional Services
o CustomizaHon § Ojen in combinaHon with license/MTaaS offerings § Data preparaHon of customer-‐owned training data § MT engine training
o Business consulHng § OpportuniHes to streamline processes § OpportuniHes to generate new business § Business consultants offer industry experience and shared industry knowledge how the new technology can be applied
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Defining the Machine TranslaHon Market
o Re-‐convergence of TM and MT o MT technology as an enabler for other business benefits or revenue generaHon
o Paradox of a vibrant MT market and a relaHve small size
o Facebook, Baidu, Google, Microsoj, Yandex, eBay are strongest MT operators without a goal of generaHng revenue from pure MT
o Focus on MT has changed from FAHQT to a tool to support global communicaHons
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Market AdopHon and Usage
o IdenHfied 65 MT operators o Largest MT providers in alphabeHcal order:
§ CSLi, Google, IBM, Lionbridge, Microsoj, PROMT, Raytheon BBN, SDL, Smart CommunicaHons, SYSTRAN.
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Market AdopHon and Usage Supplier Revenue Percentages
Server licenses 16% Desktop licenses
3%
SaaS 17%
Free 0%
Word/volume 27%
Consultancy 9%
Customization 28%
Other 0%
Revenue percentage per offering type
Excluded revenue from post-editing services
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Market AdopHon and Usage Supplier Revenue Percentages Geographical DistribuHon
North America 46%
Europe 32%
South America 2%
Asia 17%
Rest of World 3%
Revenue percentages per geography
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Market Trends
o GlobalizaHon o IntegraHon o Convergence o VerHcalizaHon o Immediacy of communicaHon o Privacy – security o High-‐quality translaHon
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Market OpportuniHes
o Business expansion o IntegraHon of MT o ProducHvity gains o MT as an enabler for new services o Narrow domain applicaHons o Customer support self-‐service
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Market Challenges
o False expectaHons – False starts o Quality of MT o Language coverage o Available training data o Specialist skills o Vendor lock-‐in o CompeHHon from free MT o Quality measurement and esHmaHon
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Drivers and inhibitors
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Seven PredicHons 1. Post-‐ediHng MT will grow very quickly and become the primary
producHon process in translaHon within five years. 2. VerHcalizaHon of MT will conHnue. Innovators will offer MT embedded
in apps and hardware to run a specific task. TranslaHon operators will differenHate themselves from free or cheap generic MT systems by developing domain and customer-‐specific engines.
3. Training and customizing MT engines will become much simpler in the next five years, making it possible for translators and project managers to train a new engine by uploading reference documents.
4. Spoken translaHon (convergence of MT with speech technology) will become widely available in the next five years.
5. MT will start playing a crucial role in Big Data, business intelligence and the Internet-‐of-‐Things.
6. The translaHon industry will start to agree on best pracHces, metrics and benchmarks for automated translaHon.
7. Access to training data becomes a bigger challenge than access to MT technology.
