1. A Syntactic Translation Memory Vincent Vandeghinste Centre for Computational Linguistics K.U.Leuven vincent@ccl.kuleuven.be

2. Translation In a globalizing world there is a growing demand for translation Multilingual websites  frequent updates  user content Shortage of human translators Speed up the translation process by means of TECHNOLOGY

3. Tools of the traditional translator Dictionaries  general dictionaries  domain specific Grammar, spelling Traditional filing system Reference material  similar documents  previous versions of the same document

4. Tools of the contemporary translator Similar to traditional translator, but electronically  Online dictionaries, dictionaries on CD-ROM  Word processors with spell and grammar checkers  Files with client specific terminology (Excel)  Reference material (PDF)

5. Tools of the contemporary translator CAT-Tools (Computer Aided Translation)  Terminology tools keep track of, list and recognize terms  Concordance Tools look up text in a corpus of translated documents  Translation Memories

6. Translation Memories A dictionary of sentences Segmentation problem: what is a sentence?  punctuation, capitalization, lay-out Retrieval of sentences  Same sentence = 100% match Sometimes even better: whole paragraphs What counts as a difference?  spaces, capitals, punctuation, lay-out  Similar sentence = fuzzy match How much difference? “Edit distance” Edit distance is measured in characters/words

7. Examples of fuzzy matches A cat sat on the mat= original A dog sat on the groundED = 66% (w) 60% (c) => 65% The cat sits on a matED = 50% (w) 53% (c) => 50% On the mat sat a catED = 50% (w) 53% (c) => 50% Cats are sitting on the matsED = 33% (w) 33% (c) => 33% Issues:  no sentence gets the threshold of 70%  every word counts the same  word variants are not recognized ED = 90% word diff + 10 % char diff

8. Machine Translation as a Tool When is MT a useful tool for the translator?  respect client specific terminology  translations are comparable with matches from translation memory  consistency  speed

9. Types of MT Rule-based – using syntax / linguistic knowledge – transformations on tree structures / interlingua – hand-made rules vs. induced rules Statistical – using no syntax, no linguistics – transformations on strings (flat) – induced translation models and language models

10. Parse and Corpus-based MT Syntactic machine translation Rule-based Data-driven: rules are induced from parallel corpus – general domain, publicly available (including Dutch) Proceedings European Parliament Translation Memory of DG-Translation (EU) OPUS corpus (subtitles, Open source manuals...) – private translation memories

11. PaCo-MT match source analysis with database of translation rules – when match is found, translation is found – when no match is found, recursively try smaller subtree, and use back-off models to connect translations of found subtrees back-off models have a lower accuracy

12. PaCo-TM match source analysis with database of translation rules – when match is found, translation is found – when no match is found, recursively try smaller subtree, and use human judgement to connect translations of found subtrees human judgement = translator judgement

13. PaCo-TM uses existing translations as source material (client specific)  specific terminlogy  the more translations RESEMBLE translations in the source material, the better the translation syntactic analysis of source and target sentences  categorization of words: verbs, adjectives, nouns... word clusters: noun phrase, infinitival phrase  lemmatization

14. Examples of syntactic fuzziness RESEMBLE = syntactical resemblance Fuzzy: not in characters or words A cat sat on the mat= original A dog sat on the ground= same syntax / insert translation of 'dog' The cat sits on a mat= same syntax / present tense / indef. mat On the mat sat a cat= same syntax / different order Cats are sitting on the mats = different structure / different phrases / different number for subject and object

15. Translation memories vs. PaCo-MT Translation memory offers translator information  Translator makes choices  Inflexible fuzzy matching PaCo-MT uses information  PaCo-MT makes choices  Syntactic flexibility in fuzzy matching  Confidence metrics can be used as threshold

16. Conclusions Flexibility of translation memories can be improved  syntactic  replace words (partial MT) Translator can set thresholds  depending on amount of domain data  depending on language pair Continuum full manual translation memory full machine translation

17. Thank you