1. CSA405: Advanced Topics in NLP Machine Translation I Introduction to MT

2. Jan 2005CSA4050 MT I2 Outline MT = Machine Translation Why MT is important What MT is and why MT is difficult MT and the Human Translator

3. Why Machine Translation is Important

4. Jan 2005CSA4050 MT I4 Implications of Multilinguality Number of Languages Number of Language Pairs 22 36 1090 20380

5. Jan 2005CSA4050 MT I5 Commerical Interest US has invested in MT for intelligence purposes MT is popular on the web - the most ued of Google's special features EU spends more that €1B per annum on translation

6. Jan 2005CSA4050 MT I6 Academic Interest Different NL technologies include –parsing –generation –morphology –pronoun resolution –understanding...

7. Jan 2005CSA4050 MT I7 Misconceptions about MT MT is a waste of time because –you will never make a machine that can translate Shakespeare. –the quality of translation you can get from an MT system is very low MT threatens the jobs of translators. MT systems are machines, and buying an MT system should be very much like buying a car.

8. Jan 2005CSA4050 MT I8 Facts about MT There are many situations where the ability to produce reliable, if less than perfect, translations at high speed is valuable. MT systems can take over some of the boring, repetitive translation jobs and allow human translation to concentrate on more interesting specialist tasks. Building an MT system is an arduous and time consuming job, involving the construction of grammars and very large monolingual and bilingual dictionaries.

9. Jan 2005CSA4050 MT I9 The Place for MT Human Translators are good at: –Getting the right turn of phrase –Preserving translation equivalence Human Translators are bad at –Dictionary look-up –Consistency of translation –Translation of terminology MT can exploit these weaknesses

10. Jan 2005CSA4050 MT I10 Summary MT is important because –There are too few human translators –Availability of materials in appropriate language has significant economic consequences. –Scientifically, it is still one of the best test areas for language technology

11. Why Translation is Difficult

12. Jan 2005CSA4050 MT I12 What Makes MT Hard Style and Meaning Word Order Word Sense Pronouns Tense Idioms

13. Jan 2005CSA4050 MT I13 Style and Meaning As recently as a decade ago it was widely believed that infectious disease was no longer much of a threat in the developed world. The remaining challenges to public health there, it was thought, stemmed from noninfectious conditions such as cancer, heart disease and degenerative diseases. Il y a une dizaine d’annees, on croyait que les pays industrialises etait debarasses des risques lies aux maladies infectieuses et que la sante publique n’etait menacee que par des maladies comme le cancer, les troubles cardiaques, et les anomolies genetiques

14. Jan 2005CSA4050 MT I14 Style and Meaning English Two sentences infectious disease was no longer much of a threat in the developed world The remaining challenges to public health there noninfectious conditions French One sentence les pays industrialises etait debarasses des risques lies aux maladies infectieuses la sante publique n’etait menacee que maladies

15. Jan 2005CSA4050 MT I15 Different word orders English word order is subject - verb - object Japanese order is subject - object - verb –English: IBM bought Lotus –Japanese: IBM Lotus bought –English: Reporters said IBM bought Lotus –Japanese: Reporters IBM Lotus bought said

16. Jan 2005CSA4050 MT I16 Word Sense Ambiguity Bank as in river Bank as in financial insitution Plant as in tree Plant as in factory Different senses usually translate into different words

17. Jan 2005CSA4050 MT I17 Hutchins & Somers (1992)

18. Jan 2005CSA4050 MT I18 Problems: Contextual Interpretation OPEN

19. Jan 2005CSA4050 MT I19 Different Cultural Models English: Health Insurance German:Krankenversicherung French: Assurance Maladie English:validate French: obliterer

20. Jan 2005CSA4050 MT I20 Differences in Marking of Semantic Information Head marking. –In English possessive relation is marked on the head: The man's house –In Hungarian it is marked on the dependent: The man house-his –his house / sa maison Direction and manner of motion marking –He ran into the room (English) –He entered the room running (French)

21. Jan 2005CSA4050 MT I21 Summary Translation is about more than equivalence of meaning. Translation may involve the resolution of ambiguity. Preservation of intention involves cultural background as well as linguistic knowledge. Translation is a hard problem – for humans let alone machines.

22. Jan 2005CSA4050 MT I22 Similarities and Differences Between Languages Differences Morphology Word order and syntactic structures Marking of semantic distinctions Lexical Similarities Communicative function for survival Mechanisms for reference to people, eating, politeness, time. Syntactic complexity Nouns Verbs

23. Machine Translation and Human Translators

24. Jan 2005CSA4050 MT I24 In the Beginning.... was the dream of FAMT Fully Automatic (High Quality) Machine Translation (Bar Hillel 1960) Source Language text Target Language text FAHQMT

25. Jan 2005CSA4050 MT I25 FAMT Basic Charactistics –No human intervention –Arbitrary text Evaluation Criteria –Quality of ouput –Cost ($/page) –Speed (pages/hour)

26. Jan 2005CSA4050 MT I26 FAMT Success Story TAUM METEO Written by Chevalier et al. 1978. Translation of weather reports from English to French Highly constrained subset of English: –Small number of senses for each word –Restricted syntactic constructions System determines whether a given sentence is within its capabilities Very fast, very accurate, no post-editing

27. Jan 2005CSA4050 MT I27 FAMT: MORAL FAMT can work well but only if we give up one or more of the goals e.g. –Unrestricted text input –High quality translation This observation has lead to research on sub-languages And to the use of FALQT

28. Jan 2005CSA4050 MT I28 FAMT is not the only way FAMT lies at one extreme of a continuum of ways in which technology can be brought to bear upon the translation problem At the other extreme there are word processing software, fax machines, and even mobile phones Between these two extremes there are other points of interest where technology can radically affect the productivity of the individual translator.

29. Jan 2005CSA4050 MT I29 MAHT and HAMT Machine Aided Human Translation (MAHT) Human Aided Machine Translation (HAMT). The essential difference between these two lies not only in the way in which the person is involved but also in the extent of their involvement

30. Jan 2005CSA4050 MT I30 MAHT - Translation Memories Systems consist of a database in which each source sentence of a translation is stored together with the target sentence (this is called a translation memory "unit") Any new source sentences will be searched for in the database and a match value is calculated. When the match value is 100%, the translation of the source sentence from the database is inserted into the text being translated.

31. Jan 2005CSA4050 MT I31 MAHT - Translation Memories If the match value is below 100% and above a certain user-definable percentage (i.e., "fuzzy match"), the old translation will be inserted as a translation proposal for the translator to review and edit. Sentences with match values below that margin have to be translated from scratch. New and changed translation proposals will then be stored in the database for future use.

32. Jan 2005CSA4050 MT I32 MAHT - Translation Memories – Advantages Avoid redoing translation of repeated material Use previous texts as a model for new translations Ensure consistency throughout a translation

33. Jan 2005CSA4050 MT I33 MAHT - Translation Memories - Drawbacks If terminology changes between projects the content of a TM needs to be updated to reflect these changes. Blind faith in exact matches (without validation) can generate incorrect translation since there is no verification of the context where the new segment is used compared to where the original one was used.

34. Jan 2005CSA4050 MT I34 MAHT - Translation Memories - Remarks Translation Process: TM tools may not easily fit into existing translation or localization processes: work best where work can be signed off in pieces rather than as a whole. Customisation: rarely works straight out of the box. Menu adaptation, filters to desktop applications may require significant effort. Investment costs are high Setup and maintenance of TMs has to factored in. OpenTag/TMX formats for exchanging TM data between competing systems

35. Jan 2005CSA4050 MT I35 MAHT – Other Technology Communication/coordination amongst translators Integration of internet technologies and web services. Database technology, smart indexing, and networking Improvements can be achieved that are well within the scope of current technology.

36. Jan 2005CSA4050 MT I36 HAMT – Human Assisted Machine Translation Machine retains the initiative but works in collaboration with human consultant. System translates autonomously until it recognises that a linguistic difficulty of a certain type has arisen, e.g. –ambiguity –pronoun reference –unknown word –unrecognised construction At this point it seeks help from the consultant.

37. Jan 2005CSA4050 MT I37 HAMT – Challenges Reliable identification/classification of difficulty. Reliable communication of difficulty to user. Tradeoff between quality and scope of translation.

38. Jan 2005CSA4050 MT I38 HAMT - Advantages Modulo challenges – a high quality of translation can be guaranteed. Speed – if large sections of text can be translated automatically. Human consultant need not necessarily have all the skills of a human translator; native competence in one or both languages may suffice.

39. Jan 2005CSA4050 MT I39 Summary Machine Translation is a continuum –FAMT –HAMT –MAHT The utility of a given type of system cannot be assessed with very simple criteria Utlility function involves at least the human cost, the machine cost, the quality of the result, and the nature of the translation requirements.

40. Jan 2005CSA4050 MT I40 Some References Jonathan Slocum, Machine Translation: its History, Current Status, and Future Prospects, Proc ACL 1984, Stanford University, http://acl.ldc.upenn.edu/P/P84/P84-1116.pdf http://acl.ldc.upenn.edu/P/P84/P84-1116.pdf Martin Kay – Machine Translation, Computational Linguistics vol 11 numbers 2-3 1985. Richard Kittredge – Sublanguages, Computational Linguistics vol 11 numbers 2-3 1985.