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Machine TranslationWiSe 2016/2017
Introduction to Machine Translation
Dr. Mariana Neves October 17th, 2016
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT paradigms
● MT course
2
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT paradigms
● MT course
3
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Machine translation (MT)
● Automatic translation from one language to another
● Koehn: „Translating between languages is […] a task for which even humans require special training.“
4
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Machine translation
5
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6[From Jurafski and Martin 2009]
Machine Translation
C1: DAIYU ALONE ON BED TOP THINK BAOCHAI
E1: As she lay there alone Daiyu's thoughts turned to Baochai .
C3: CLEAR COLD PENETRATE CURTAIN
E3: The coldness penetrated the curtains of her bed .
C4: NOT FEELING FALL DOWN TEARS COME
E4: Almost without noticing it she had began to cry .
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT paradigms
● MT course
7
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MT applications
8
Assimilation
Dissemination Communication
[Koehn 2010]
Understand the content
Publication inother languages
Emails, chats
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Fully Automatic High Quality Machine Translation
(FAHQMT)
● Limited domains (weather, sport, rail, flight info)
● Controlled vocabulary
9
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10(http://rali.iro.umontreal.ca/rali/?q=en/Meteo)
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Controlled languages - rules
11 (http://works.bepress.com/cgi/viewcontent.cgi?article=1126&context=uwe_muegge)
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Gisting
12
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Gisting
13(http://www.morgenpost.de/)
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Gisting for intelligence agencies
14
(http://info.moravia.com/blog/bid/193094/U-S-defense-projects-may-drive-innovations-in-machine-translation http://www.slate.com/articles/technology/future_tense/2012/05/ darpa_s_transtac_bolt_and_other_machine_translation_programs_search_for_meaning_.htmlhttp://www.wired.com/2011/04/militarys-newest-recruit-c-3p0/)
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Gisting for intelligence agencies
● As a first step, select relevant documents from a large collection.
● Interesting documents will then be passed to a human translator
15
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Integration with speech technologies
16(http://www.skype.com/en/translator-preview/)
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Integration with speech technologies
17 (http://www.itproportal.com/2012/06/26/how-to-broadcasting-your-business-presentation-anywhere-in-the-world/)
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Hand-held devices
18
police military medical tourism
(http://www.ectaco.translation.net/)
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Hand-held devices
19
(http://www.ectaco.translation.net/http://www.amazon.com/Bidirectional-Electronic-Dictionary-PhraseBook-Handheld/dp/B001OTMELY https://play.google.com/store/apps/details?id=com.google.android.apps.translate&hl=en)
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Tools for translators,
Post-editing
20
(http://www.languagestudio.com/LanguageStudioDesktop.aspx http://www.asiaonline.net/EN/MachineTranslation/default.aspx?QID=21)
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT paradigms
● MT course
21
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Typology
● Study of cross-linguistic similarities and differences
● Morphology
– Agglutinative
● Turkish
– Fusion
● Spanish
22 (http://allthingslinguistic.com/post/50939757945/morphological-typology-illustrations-from)
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Typology
● Syntax: e.g., order of verb (V), subject (S) and object (O)
23
彼女は音楽を聴いて大好き。(she music to listening adores)
SVO:(German, French, English, Mandarin)
She adores listening to music.
SOV:(Hindi, Japanese)
VSO: (Irish, Arabic, Biblical Hebrew)
Dúil mhór aici éisteacht le ceol.(adores she music to listen)
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Typology
● Argument structure and linking
– Head-marking:
– „the man's house“ (English)
– Dependent-marking:
– „A férfi házában“ „the man house-his“ (Hungarian)
24
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Typology
● Verbs and satellite particles (direction, motion, etc.)
● Verb-framed:
– Spanish: „La botella salió flotando“ (The bottle exited floating.)
● Satellite-framed
– English: „the bottle floated out“
25
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Typology
● Pronouns omission
– Pronoun-drop:
● English: [I] am reading a book.● Spanish: Estoy leyendo un libro.
26
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Typology
● Pronouns omission
– Referential density
● Cold: more inferential work to recover antecedents
– Japanese, Chinese● Hot: more explicit and easier
– Spanish
27
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Lexical
● Homonymy
– wall (Wand), wall (Mauer)
● Polysemy
– to know (knowing a fact) : wissen
– to know (familiarity with a person/location): kennen
28
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Lexical
● Grammar
– English: „She likes to sing“
– German: „Sie singt gern.“
● Lexical gap
– „A world view, a philosophy of life“ – Weltanschauung
29(http://abbysroad.tumblr.com/post/12947835861/an-incomplete-list-of-english-lexical-gaps)
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Other divergences
● Position of adjectives
– English: „green witch“
– Spanish: „bruja verde“ - „witch green“
30
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Other divergences
● Cultural aspects, e.g., calendars and dates
– British English: DD/MM/YY
– American English: MM/DD/YY
– Japanese: YYMMDD
31
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT paradigms
● MT course
32
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First references to MT
33
(http://www.biography.com/people/ren-descartes-37613https://en.wikipedia.org/wiki/Gottfried_Wilhelm_Leibniz)
As early as the 17th century by philosophers René Descartes and Gottfried Wilhelm Leibniz
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First references to MT
34
In 1947, Warren Weaver and Andrew Booth suggested that computers could be used to translate natural languages.
(http://apprendre-math.info/history/photos/Weaver.jpeg http://www.dcs.bbk.ac.uk/about/history/booth.php)
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Post WWII:
foreign languages as encrypted English
35
“One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: 'This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.'”"Translation" (1955), in W.N. Locke and A.D. Booth (eds.), Machine Translation of Languages (MIT Press, Cambridge, Mass.).”
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Georgetown-IBM experiment (1954)
● „[...] human translations were subject to political bias and interference“
● Translation of 60 sentences from Russian into English
● Topic: organic chemistry
● System: six grammar rules and 250 words in the vocabulary
36
(Report: http://www.hutchinsweb.me.uk/GU-IBM-2005.pdf http://www.thelinguafile.com/2013/10/the-georgetown-ibm-experiment-rise-of.html#.Vehb1t93nq5)
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Georgetown-IBM experiment (1954)
● Conclusions
– The problem was solved
– But semantic disambigution are impossible to be solved automatically
37
(https://en.wikipedia.org/wiki/Georgetown-IBM_experiment)
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ALPAC report (1966)
● Automatic Language Processing Advisory Committee
● Study of reality of MT
● Conclusions:
– post-editing not cheaper than full translation
– Little Russian scientific literature worth to be translated
– No shortage of human translators
– No advantage in using machine translation
– Better fund linguistic research for human translation
● Funding for MT stopped in the US as a consequence
38(http://www.hutchinsweb.me.uk/MTNI-14-1996.pdf)
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History of MT
● 1970s, first commercial systems
– Météo
– Systran
– Logos
– METAL
– Trados
39
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First commercial systems
● 1968: Founded by Dr. Peter Toma
● 1969: US Air Force - scientific and technical documents Russian/English
● 1975: Commission of European Communities (CEC)
● 1976: CEC – system from English/France
● 1981: CEC – English/French, French/English, English/Italian
● 1986: Xerox – six target languages
● 1985: SYSTRAN PRO for Windows
● 1997: search engine AltaVista's (today Yahoo's)
● 2006-2007:
40 (http://www.thelinguafile.com/2013/11/systran-brief-history-of-machine.html#.VehiWd93nq4)
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History of MT
● 1980s, 1990s: interlingual systems
41(http://www.dictionarybarn.com/img/Interlingual-Machine-Translation.jpg)
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Data-driven methods
● 1980s, Example-based translation
42(http://ilk.uvt.nl/mbmt/pbmbmt/)
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Data-driven methods
● Late 1980, Statistical machine translation
43 (http://www.themarysue.com/how-does-google-translate-work/)
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Current commercial developers
44
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Overview
● Introduction
● Applications
● History
● Challenges
● Available resources
● MT paradigms
● MT course
45
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Available resources
● Tools
● Parallel corpora
46
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Tools
● Natural language processing (NLP) tools:
– Tokenization, parsing, named-entity recognition
● MT tools:
– GIZA++: IBM's word-based models
– Moses, Thot: phrase-based models
● MT evaluation tools:
– BLEU, METEOR
47
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Parallel corpora
● LDC, Gigaword
48
(https://catalog.ldc.upenn.edu/LDC2011T11 https://catalog.ldc.upenn.edu/LDC2011T07)
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Parallel corpora
● Europarl
49(http://www.statmt.org/europarl/)
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Parallel corpora
● Acquis Communautaire
50 (http://optima.jrc.it/Acquis/JRC-Acquis.2.2/doc/README_Acquis-Communautaire-corpus_JRC.html)
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Parallel corpora
51 (http://www.scielo.br/)
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Evaluation campaigns
52
(http://www.nist.gov/itl/iad/mig/openmt15.cfm)
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Evaluation campaigns
53(http://workshop2015.iwslt.org/59.php)
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Evaluation campaigns
54
(http://www.statmt.org/wmt16/)
HPI at the WMT’16,WMT'17!
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT paradigms
● MT course
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MT paradigms
56
Machine Translation
Knowledge-based MT
Rule-based MT
Data-driven MT
Example-based MT Statistical MT Neural MT
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Rule-based MT
57
(https://nlp.fi.muni.cz/web3/en/MachineTranslation)
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Rule-based MT
● Apertium (https://www.apertium.org)
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Example-based MT
59
(http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?plugin=attach&refer=KUROHASHI-KAWAHARA-LAB&openfile=EBMT.png)
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Example-based MT
● Cunei (http://cunei.sourceforge.net/)
● KyotoEBMT (http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?KyotoEBMT)
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Statistical MT
61
(https://nlp.fi.muni.cz/web3/en/MachineTranslation)
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Statistical MT
● Moses (http://www.statmt.org/moses/)
● Cunei (http://cunei.sourceforge.net/)
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Neural MT
63
(https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/)
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Neural MT
● LISA (http://104.131.78.120/)
● TensorFlow (https://research.googleblog.com/2016/09/a-neural-network-for-machine.html)
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Overview
● Introduction
● Applications
● Challenges
● History
● Available resources
● MT course
65
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MT course – what to expect from me
● Overview on MT methods
● Supervision of the projects
● Be available by email and in the office (Villa room 0.01)
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MT course – what I expect from you
● Presence and participation in the lecture (not controlled)
● Take part in a project (team or individual)
● Take part in the final exam
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Project
● Teams of 2/3 students
● „Take part“ in one of the translation challenges at WMT‘16 (http://www.statmt.org/wmt16/)
● News
● IT-domain
● Biomedical
● Presentation of preliminary and final results
● Submission of a 3-pages report
● Source code in GitHub or similar
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Project
● Flexible...
● „Any“ translation task (first-come, first-served)
● Any language pair
● Any MT paradigm
● Any NLP/MT tools
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Project
● ...but with some requirements
● Integration of domain-specific resources
● Training on out-of-domain corpora (talk to other teams)
● Evaluation of official test datasets (last year‘s test data)
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Project
● Mail to me ([email protected]):
● Team members
● WMT translation task(s)
● Language pair(s)
● Host of the project (GitHub, etc)
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72 (https://hpi.de//en/plattner/teaching/winter-term-201617/machine-translation.html)
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Grading
● 60% Project
● Commitment, implementation, presentation, report
● Each team member should present in either of the two appointments (mid-term or final)
● 40% Final exam
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Course books
● Statistical Machine Translation
– Philipp Koehn
● Machine Translation
– Pushpak Bhattacharyya
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Course books
● Speech and Language Processing (Chapter 25)
– Daniel Jurafsky and James H. Martin
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Course books (advanced topics)
● Learning Machine Translation
– Edited by Cyril Goutte, Nicola Cancedda, Marc Dymetman
76
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Workshop papers
77
(http://www.statmt.org/wmt16/papers.html)