dr. preslav nakov — combining, adapting and reusing bi-texts between related languages —...
DESCRIPTION
Bilingual sentence-aligned parallel corpora, or bitexts, are a useful resource for solving many computational linguistics problems including part-of speech tagging, syntactic parsing, named entity recognition, word sense disambiguation, sentiment analysis, etc.; they are also a critical resource for some real-world applications such as statistical machine translation (SMT) and cross-language information retrieval. Unfortunately, building large bi-texts is hard, and thus most of the 6,500+ world languages remain resource-poor in bi-texts. However, many resource-poor languages are related to some resource-rich language, with whom they overlap in vocabulary and share cognates, which offers opportunities for using their bi-texts. We explore various options for bi-text reuse: (i) direct combination of bi-texts, (ii) combination of models trained on such bi-texts, and (iii) a sophisticated combination of (i) and (ii). We further explore the idea of generating bitexts for a resource-poor language by adapting a bi-text for a resource-rich language. We build a lattice of adaptation options for each word and phrase, and we then decode it using a language model for the resource-poor language. We compare word- and phrase-level adaptation, and we further make use of cross-language morphology. For the adaptation, we experiment with (a) a standard phrase-based SMT decoder, and (b) a specialized beam-search adaptation decoder. Finally, we observe that for closely-related languages, many of the differences are at the subword level. Thus, we explore the idea of reducing translation to character-level transliteration. We further demonstrate the potential of combining word- and character-level models.TRANSCRIPT
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Combining, Adapting and Reusing Bi-texts between Related Languages:
Application to Statistical Machine Translation
Preslav Nakov, Qatar Computing Research Institute(collaborators: Jorg Tiedemann, Pidong Wang, Hwee Tou Ng)
Yandex seminarAugust 13, 2014, Moscow, Russia
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Plan
•Part I- Introduction to Statistical Machine Translation
•Part II- Combining, Adapting and Reusing Bi-texts between Related
Languages: Application to Statistical Machine Translation
•Part III- Further Discussion on SMT
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Machine Translation:Hard or Easy?
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Why is Machine Translation Hard?
•Word order- En: I want beer.- Tr: Ben bira istiyorum.
•Lexical ambiguity- Ru: Штирлиц топил печку. Через час печка утонула.
•Pronouns, coreference- Ru: Если ребенок не любит холодное молоко, сварите его.- En: If the baby does not like cold milk, boil it/him.
•Idioms- Ru: Петр приказал долго жить.- En: Peter kicked the bucket.
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Why is Machine Translation Hard?
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Natural Language is Ambiguous
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Ambiguity in Russian: Idioms
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Ambiguity in Russian: Names
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Ambiguity in Russian: Stress
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Russian Jokes about Stierlitz
•Штирлиц топил печку. Через час печка утонула.•Встретив гестаповцев, Штирлиц выхватил шашку и закричал: "Порублю!" Гестаповцы скинулись по рублю и убежали.•Штирлиц шёл по лесу и увидел голубые ели. Штирлиц присмотрелся и увидел, что голубые не только ели, но и пили.•Штирлиц подошёл к окну. Из окна дуло. Штирлиц закрыл окно. Дуло исчезло.
http://olgakagan.blog.com/2012/01/28/homonymy-in-russian-jokes-about-stierlitz/
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Russian Jokes about Stierlitz
http://olgakagan.blog.com/2012/01/28/homonymy-in-russian-jokes-about-stierlitz/
•Штирлиц выстрелил в Мюллера в упор. Мюллер не упал. “Броневой,”- подумал Штирлиц.•Лампа светила, но света не давала. Штирлиц погасил лампу и Света дала.•Штирлиц шёл по лесу и наткнулся на сук. “Шли бы вы домой, девушки. Война всё-таки!”•Штирлиц лёг на гальку. Галька вскрикнула и убежала.•Штирлиц сел в машину. "Всё, можно трогать!"- сказал он. "Ого-го!"- потрогала Кэт.
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When is Machine Translation Easy?
•Very closely related languages- similar word order, grammar
•Legal Texts- many repetitions
•Caterpillar English- simplified to make MT easy
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Translating a European Convention: English Bulgarian
English (orig.) Human Translation Computer Translation
European Convention on Mutual Аssistance in Criminal MattersPreambleThe governments signatory hereto, being members of the Council of Europe, considering that the aim of the Council of Europe is to achieve greater unity among its members;believing that the adoption of common rules in the field of mutual assistance in criminal matters will contribute to the attainment of this aim;considering that such mutual assistance is related to the question of extradition, which has already formed the subject of a convention signed on 13th december 1957,have agreed as follows:
Европейска конвенция за взаимопомощ по наказателно-правни въпросиПреамбюлПравителствата, подписали тази конвенция, в качеството си на членове на Съвета на Европа,считайки, че целта на Съвета на Европа е да се постигне по-голямо единство между неговите членове,убедени, че приемането на общи правила в областта на правната помощ по наказателни дела ще допринесе за постигането на тази цел,считайки, че правната помощ е свързана с въпроса за екстрадицията, която вече бе предмет на конвенцията, подписана на 13 декември 1957 година, се споразумяха за следното:
Европейска конвенция за взаимопомощ по наказателно-правни въпросиПреамбюлПравителствата, подписали този протокол, членове на Съвета на Европа,
считайки, че целта на Съвета на Европа е постигането на по-голямо единство между своите членове,убедени, че приемане на общи правила в областта на правна помощ по наказателни дела ще допринесе за постигането на тази цел,считайки, че тази взаимна помощ е свързана с въпроса за екстрадиция, който вече е образувано предмет на конвенция, подписана в 13th декември 1957 година,се споразумяха за следното:
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Adapting „Macedonian“ to Bulgarian„Macedonian” Human Translation Computer Translation
СКОПЈЕ, Македонија -- Според дипломатски извори, првата мировна мисија на ЕУ, која ќе биде распоредена во Македонија на 31-ви март, ќе го носи името Конкордија.Околу 27 земји навестија подготвеност да учествуваат во шестмесечната мисија.Се очекува македонскиот Парламент в среда (26-ти март) да го одобри нацрт договорот за статусот на силите на ЕУ, со кој на трупите на Унијата им се гарантира дипломатски статус и имунитет.Во вторникот, во меѓувреме, во Скопје започна меѓународна конференција за децентрализација, при што заменик-премиерот Муса Џафери го одржа воведниот говор.Во другите вести, претседателот Борис Трајковски за Утрински весник потврди дека експертски тим на министерствата за внатрешни работи и за одбрана ја завршил својата работа на првата национална стратегија за безбедност и одбрана.
СКОПИЕ, Македония -- Първата мироопазваща мисия на ЕС, която ще бъде разположена в Македония на 31 март, ще се нарича "Конкордия", съобщиха дипломатически източници.Тя ще продължи шест месеца. Около 27 страни са заявили желание да участват нея.Очаква се македонският парламент да одобри проекта на Споразумението за статута на силите в сряда (26 март), гарантирайки по този начин дипломатически статут и имунитет на съюзническия контингент.Междувременно, във вторник в Скопие започна международна конференция по въпросите на децентрализацията. Тя бе открита с реч на вицепремиера Муса Джафери.В други новини, президентът Борис Трайковски потвърди за Утрински весник, че екипът от експерти към министерствата на вътрешните работи и отбраната е завършил работата си по проекта за националната стратегия за сигурност и отбрана.
СКОПИЕ, Македония - Според дипломатически източници, първата мироопазваща мисия на ЕС, която ще бъде разположена в Македония на 31 март, ще носи името на Конкордия.
Около 27 страни намекнаха готовност да участват в шестмесечната мисия.
Очаква се македонският парламент в сряда (26 март) да одобри проект на споразумението за статута на силите на ЕС, с който на войниците на съюза да се гарантира дипломатически статут и имунитет.
Междувременно, във вторник в Скопие започна международна конференция, посветена на децентрализацията и вицепремиерът Муса Джафери изнесена изказването.
Други новини, президентът Борис Трайковски за Утрински вестник, че експертен екип на министерствата на вътрешните работи и отбраната е завършил работата си на първата национална стратегия за сигурност и отбрана.
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Summary: Machine Translation Today
•Usable technology- Translation memories- Web translation- “Caterpillar” English
•New profession- post-editor
•MT will never replace human translators- Computers cannot be held legally responsible
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Big CompaniesCare About SMT
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SDL – First To Invest In SMT
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Lionbridge Partners with IBM
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Facebook Buys SMT Company
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Old-Timer Systran Finds New Home
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eBay Considers SMT To Open New Markets
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Adobe Supports Open Source SMT
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Intel Investigates MT
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The Big Dream of NLP
Dave Bowman: “Open the pod bay doors, HAL”
HAL 9000: “I’m sorry Dave. I’m afraid I can’t do that.”
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Future Directions
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Two Important Directions
•Semantics
•Machine Translation
Critical for the overalladvancement of the field
Practical, within the reachof current technology
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Two Important Directions
•Semantics
•Machine Translation
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Semantics: Revolution is Needed?
•If we want the dream come true, we should- not rely on superficial statistics alone- need to get to the meaning of text
•A revolution in semantics is needed- looking at words is not enough- we need better models for
o multi-word expressions (~70% of terminology)o semantic relations (meaning is in the links!)
•The revolution will be supported by- Web-scale corpora- linguistic knowledge
“Moving Lexical Semanticsfrom Alchemy to Science”
Discussion on [Corpora-List]
• This is what Chomsky has done with syntax.• Should we expect the same for lexical semantics?
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Two Important Directions
•Semantics
•Machine Translation
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Machine Translation
•The task that started the whole NLP field
•The hottest research topic today
•High practical and economic expectations
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Machine Translation: Evolution?
•Evolution?- Resource-poor language pairs- Morphologically rich languages
- Smarter Web-scale translation models
- Noisy inputo spoken languageo emails, chats, forums, Twittero poetry
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Machine Translation: Revolution?
•Revolution?- Two great revolutions so far
o1993: statistical word-based translationo2003: statistical phrase-based translation
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Machine Translation: Revolution?
•Revolution?- Two great revolutions so far
o1993: statistical word-based translationo2003: statistical phrase-based translation
- Overdue for the next revolution?o2013: ???
• Syntactic translation?• Semantic translation?
SOURCE TARGET
words words
syntax syntax
semantics semantics
interlingua
phrases phrases
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Machine Translation: Revolution?
•Revolution?- Two great revolutions so far
o1993: statistical word-based translationo2003: statistical phrase-based translation
- Overdue for the next revolution?o2013: ???
• Syntactic translation?• Semantic translation?
SOURCE TARGET
words words
syntax syntax
semantics semantics
interlingua
phrases phrases
Or maybe a return of deep neural networks?• Already started a little revolution in speech recognition• Very strong results for SMT, best paper award at ACL’2014
(Devlin&al., ACL 2014)• Very strong results for semantics too (word embedding)
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The Future?
Three words: Web, semantics, linguistics
and deep neural networks?
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QCRI
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Qatar
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Qatar
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EMNLP 2014
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Qatar
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Vision and Mission
Become a world-leader in Arabic language technologies
Conduct innovative and strategic research and development
with local and global impact.
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The ALT Research Areas
• Build strong foundation: Arabic NLP• 2 flagship projects • 3 supplementary focus areas
motivated by local needs
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Dr. Preslav NakovSenior Scientist
Ahmed AliSenior Software Eng.
Abdulrahman GhanemSoftware Engineer
Dr. Kareem DarwishSenior Scientist
Dr. Stephan VogelPrincipal Scientist
Dr. Francisco GuzmanScientist
Dr. Walid MagdyScientist
Dr. Hassan SajjadScientist
Dr. Shafiq JotyScientist
Dr. AlessandroMoschittiSenior Scientist
Yifan ZhangSenior Software Eng
Dr Ahmed AbdelaliSenior Software Eng
Hamdy MubarakSenior Software Eng.
Dr. Lluis MarquezPrincipal Scientist
Dr. Ferda OfliScientist
The ALT Team
Fahad Al-ObaidliResearch Assistant
plus interns …
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Machine Translationat QCRI
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Speech Translation
News
Lectures
Meetings
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Application: News Translation
•Objective: high quality speech recognition and translation•Enable video search•Collaboration with Aljazeera
http://alt.qcri.org/QCRI_Demo/Speech_Recognition.html
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Application: Lecture Translation
•Objective: enable wider reach of educational material•Primarily English -> Arabic
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Application: Meeting Translation
•Objective: real-time, low-latency translation•Flexible architecture based on cutting-edge technology
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The Future: „Ubiquitous“ Translation
Google glass
Smart glass
صحح؟
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Acknowledgments
•Used some slides by George Doddington, John Hutchins, Kevin Knight, Jonas Kuhn, Dan Klein, Philipp Koehn, Daniel Marcu, Drago Radev, Arturo Trujillo, Stephan Vogel, C. Wayne, Kenji Yamada, etc.