Future work on adaptive computer-assisted translation (拋磚引玉; throwing brick to reveal jade) for TAUS Tokyo 2014
•
1 recomendación•604 vistas
What can and cannot be done with current computer-assisted translation tools?
Recomendados
Más contenido relacionado
Similar a Future work on adaptive computer-assisted translation (拋磚引玉; throwing brick to reveal jade) for TAUS Tokyo 2014
Similar a Future work on adaptive computer-assisted translation (拋磚引玉; throwing brick to reveal jade) for TAUS Tokyo 2014 (19)
Último
Último (20)
Future work on adaptive computer-assisted translation (拋磚引玉; throwing brick to reveal jade) for TAUS Tokyo 2014
- 4. Outline Full of trivial (embarrassing?) points 4
- 5. – Ed Hovy “A plague of statistics has descended on our houses.” 5 e.g. 11,001 New Features for Statistical Machine Translation……
- 6. — George E. P. Box “Essentially, all models are wrong, but some are useful.” 6
- 9. First Brick in the Wall • Via Negativa • False positive/negative • Error propagation • Unknown unknown 9
- 10. Funny Autocomplete “autocomplete is not a function” is current top-1 Google autocomplete of “autocomplete is”. 10
- 11. Autocomplete is NOT a function • Neither is auto-suggestion • They are many-to-many relations with scores. • Recognize this? 11
- 12. Many-to-many Scoring • Map by prefix, rank by popularity • Google search box autocomplete • Map by occurrence, rank by similarity • Search (information retrieval) • Map by information, rank by knowledge • Translation 12
- 13. Information? • Surface patterns and…… • Imaginations • Quantum information theory • Tensor (Network Algorithm) • Quantum Physics and Linguistics • Frobenius (diagrammatic) algebras (for semantics) 13
- 14. Knowledge? Black swan…… OK, too philosophical now. 14
- 15. Popularity & Similarity • Popularity: famous or infamous? • Consensus: social choice? • Similarity • Distance: rational choice? 15
- 16. Prefix, Occurrence • Surface pattern • Regular 16 • Context-free • Context-sensitive • Recursively blahblah……
- 17. Map & Rank • Regular expression • Edit distance 17
- 18. Regular expression • [a-z]+ • Colours of cats and dogs. • [^o]{2} • Colours of cats and dogs. • cat|dog • Colours of cats and dogs. • Colou?rs? • Colours of cats and dogs. • Colors of cats and dogs. • Color of a cat. • <[A-Za-z][A-Za-z]*> • <html>Colours of cats and dogs.</html> 18
- 19. Edit Distance • Colors • Delete s • Color • Insert u • Colour • Replace C with c • colour • Distance from Colors to colour: 3 (or 4 if the cost of replacing is 2) 19
- 20. – One may ask “What if I wanted to map 1,１, one, and ONE?” 20
- 21. Normalization • time flies like an arrow. fruit flies like bananas. • Case restoration • Time flies like an arrow. Fruit flies like bananas. • Sentence segmentation • time flies like an arrow. • fruit flies like bananas. • Word normalization: stemming or lemmatization? 21
- 22. Stemming • Porter Stemmer (mainly suffix stripping) • flies → fli • bananas → banana • How about “flies → fly”? • Lemmatization 22
- 23. Lemmatization • flies → fly • better → good • meeting • meet? • axes • axe? • axis? 23
- 24. Stemming or lemmatization, which is better? “Battlestar Galactica is frakking wierd.” 24
- 25. Are we doing good? Evaluate it! 25
- 26. Confidence Score • Confidence interval? Confidence level? • Not really • But it can be • Just a buzz word from speech recognition • Shannon’s game • Hidden-Markov models • Generative • The Italian who went to Malta • Can be any reasonable score • Mostly probability 26
- 27. Calculate Sentence Similarity Confident Trusted Doubted [partial match] [exact match] [no match] a / b < threshold, since b is higher when a = prob. of ( #2(w1 w2 w3 w4) #1(w1 w2 w3) #1(w2 w3 w4) #1(w1 w2) #1(w2 w3) #1(w3 w4) #2(w1 w3) #2(w2 w4) #3(w1 w4)); b = avg. prob. of all known exact matches; where #n: any other (n - 1) words in-between. Sentence:“w1 w2 w3 w4.” 27
- 28. Evaluate Pair: {Source,Target} Confidence Confident Trusted Doubted [Trusted Source] [Confident Source] [Doubted Source] Triple: {Source,Target, Back} Source Target [Trusted Target] [Not Doubted Target] Evaluate Back Confidence [Doubted Back] 28
- 30. Summarization • Extraction • Classification • Discriminative • Abstraction • Aggregation • Generative 30
- 31. The name of the rose Sounds depressing? Let’s try it anyway…… 31
- 32. How about voting? Consensus and prediction: non-linear programming 32
- 33. Sentiment Analysis • Classification • Polarity • やばい • Subjectivity • In my opinion…… • Emotion 33
- 34. Semantics? • Classification vs. • Ranking (as we’ve seen so far) • Clustering • Regression • …… 34
- 35. Even Intractable • Minimum Feedback Arc Set • NP-complete, APX-hard • Bipartite Tournament • Hypergraph Grammar • Synchronous Grammar • Arrow’s Impossibility Theorem • Social Choice • Voting System 35
- 36. – disputed “Prediction is very difficult, especially about the future.” 36
- 37. There are two kinds of… PAIN. The sort of pain that makes you strong, or useless pain. The sort of pain that's only suffering. I have no patience for useless things. 37
- 38. What might make me stronger…… (See also http://www.no-free-lunch.org) 38
- 39. Website Translation 250~ S&B sites / 3 months: ~50% are compatible, 2 have paid 39
- 40. Different Story NY-based, IT capable (See also https://dakwak.com) 40
- 41. HTML Side-effect <span class=“notranslate”>Hello, WorldJumper!</span> <!-- Are you talking to me? --> 41
- 42. I want more info Less is more. 42
- 43. [[[坂⻄西優]]]です。 • [[[坂⻄西優=Suguru Sakanishi]]]です。 • • • • • • • 43
- 44. More Anomalies • 【⽶米】 • 飛来物 • 菜の花 • 桃⽩白⽩白 • ⽩白⽴立斌 • Oh, I also want [[[this part to be a partially matched TM]]] pre-edited for MT, please? 44
- 45. Read my lips It’s not only about sound 45
- 46. Transliteration is not…… • Romanization • Transcription 46
- 47. Transliteration • Alignment • Alignment • Alignment • (And better be more than bilingual) 47 (1) er of n the and ence also s or of to-one-alignments possible. Furthermore, combine to produce a single phoneme (d single letter can sometimes produce tw phonemes). For example, the English wo Chinese transliteration “ ”, which “phonemes”, is aligned as [15]: A BE RT | | |
- 48. <!-- ⽩白⽴立斌 --> Hey! How about my privacy? 48
- 49. Overwriting • <!-- John Doe #1 --> 49
- 51. Slot Machine Email Template? Rule-based Machine Translation? 51
- 53. Reinforcement • Explore vs. Exploit • Interactive • Online • Free Lunches • Second moments and higher of algorithms' generalisation error • Coevolution • Confidence intervals can give a priori distinctions between algorithms • People respond to incentives 53
- 54. Translate X for Y • {restaurant AD, coupon} • {game, credit} • {subtitle, DRM-free video} • {Heart Sūtra, inner peace} • {inside news, outside support} • Taiwanese protesters • {anything, incentives} • See also: Unbabel, Duolingo 54
- 55. New Types of Assistance for Translators by Philipp Koehn (http://www.mastar.jp/wfdtr/shiryou2013/Philipp%20Koehn.pdf via http://www.mastar.jp/wfdtr/index-e.html) 55
- 57. Wrap up • Where’s my pony semantics? • Adaptation • Chinese restaurant process • Indian buffet process • 信 (adequate)、達 (fluent) • 雅 (elegant)？貼 (pertinent)？ • Bilingual might be insufficient: 全⽇日空 → ANA • Pony: you can’t always get what you want • Extrinsic evaluation • Embrace and enjoy changes 57
Notas del editor
- Discount Rule overlap Bad single-level rewrites Node count Insertion Soft syntactic constraints Structural distortion Word context
- Well, thanks (?) to Chomsky