Computer Aided Translation
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1 Computer Aided Translation Philipp Koehn 30 April 2015
2 Why Machine Translation? 1 Assimilation reader initiates translation, wants to know content user is tolerant of inferior quality focus of majority of research Communication participants don t speak same language, rely on translation users can ask questions, when something is unclear chat room translations, hand-held devices often combined with speech recognition Dissemination publisher wants to make content available in other languages high demands for quality currently almost exclusively done by human translators
3 Why Machine Translation? 2 Assimilation reader initiates translation, wants to know content user is tolerant of inferior quality focus of majority of research Communication participants don t speak same language, rely on translation users can ask questions, when something is unclear chat room translations, hand-held devices often combined with speech recognition Dissemination publisher wants to make content available in other languages high demands for quality OUR currently almost exclusively done by human translatorsfocus
4 Goal: Helping Human Translators 3 If you can t beat them, join them. How can machine translation help human translators?
5 Post-Editing Machine Translation 4 (source: Autodesk)
6 Machine Translation Quality Matters 5 Experiment: Post-editing with different machine translation systems English German, news stories UEDIN SYNTAX ONLINE B UEDIN PHRASE OTHER WMT sec/word 5.46 sec/word 5.45 sec/word 6.35 sec/word 0 sec/word 2 sec/word 4 sec/word 6 sec/word [Koehn and Germann, 2014]
7 Overview 6 Interactivity Choices Confidence Adaptation
8 Interactivity 7 Traditional professional translation approaches translation from scratch post-editing translation memory match post-editing machine translation output More interactive collaboration between machine and professional?
9 Interactive Machine Translation 8 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator
10 Interactive Machine Translation 9 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He
11 Interactive Machine Translation 10 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He has
12 Interactive Machine Translation 11 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He has for months
13 Interactive Machine Translation 12 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned
14 Interactive Machine Translation 13 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned for months
15 Prediction from Search Graph 14 planned for months he has for months it has since months Search for best translation creates a graph of possible translations
16 Prediction from Search Graph 15 planned for months he has for months it has since months One path in the graph is the best (according to the model) This path is suggested to the user
17 Prediction from Search Graph 16 planned for months he has for months it has since months The user may enter a different translation for the first words We have to find it in the graph
18 Prediction from Search Graph 17 planned for months he has for months it has since months We can predict the optimal completion (according to the model)
19 time 80ms Run Time 18 72ms 64ms 56ms 48ms 7 edits 8 edits 56 edits 4 edits 40ms 32ms 3 edits 24ms 16ms 8ms 0ms 2 edits 1 edit 0 edits prefix 40
20 Word Alignment Visualization 19 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned for months to give a lecture in Baltimore in
21 Word Alignment Visualization 20 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned for months to give a lecture in Baltimore in
22 Shading off Translated Material 21 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned for months to give a lecture in Baltimore in
23 Some Observations 22 How can we do this? word alignments by-product of matching against search braph automatic word alignments (as used in training) User feedback users like interactive machine translation... but they may be slower than with post-editing machine translation user like mouse-over word alignment highlighting user do not like at-cursor word alignment highlighting
24 Overview 23 Interactivity Choices Confidence Adaptation
25 Choices 24 Trigger the passive vocabulary Display multiple translations for words and phrases er hat seit Monaten geplant, im März einen Vortrag... he has for months the plan in March a lecture... it has for months now planned, in March a presentation... he was for several months planned to in the March a speech... he has made since months the pipeline in March of a statement... he did for many months scheduled the March a general... Rank and color-highlight by probability of each translation Prefer diversity
26 Alternative Translations 25 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned for months to give a lecture in Baltimore in April. give a presentation present his work give a speech speak User requests alternative translations for parts of sentence.
27 Bilingual Concordancer 26
28 Overview 27 Interactivity Choices Confidence Adaptation
29 Confidence 28 Machine translation engine indicates where it is likely wrong (also known as quality estimation Lucia Specia) Different Levels of granularity document-level (SDL s TrustScore ) sentence-level word-level What are we predicting? how useful is the translation on a scale of (say) 1 5 indication if post-editing is worthwhile estimation of post-editing effort pin-pointing errors
30 Sentence-Level Confidence 29 Translators are used to Fuzzy Match Score used in translation memory systems roughly: ratio of words that are the same between input and TM source if less than 70%, then not useful for post-editing We would like to have a similar score for machine translation Even better estimation of post-editing time estimation of from-scratch translation time can also be used for pricing Active research question, see also shared task at WMT 2013
31 Word-Level Confidence 30 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Machine Translation He has for months planned in April give a lecture in Baltimore.
32 Word-Level Confidence 31 Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Machine Translation He has for months planned in April give a lecture in Baltimore. Note: different color for wrong words and reordered words (inserted words? missing words?)
33 Automatic Reviewing 32 Can we identify errors in human translations? missing / added information inconsistent use of terminology Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Human Translation Moreover, he planned for months to give a lecture in Baltimore.
34 Overview 33 Interactivity Choices Confidence Adaptation
35 Adaptation 34 Machine translation works best if optimized for domain Typically, large amounts of out-of-domain data available European Parliament, United Nations unspecified data crawled from the web Little in-domain data (maybe 1% of total) information technology data more specific: IBM s user manuals even more specific: IBM s user manual for same product line from last year and even more specific: sentence pairs from current project Various domain adaptation techniques researched and used
36 Incremental Updating 35 source text translate MT engine MT translation re-train post-edit human translation
37 Incremental Updating 36 source text translate MT engine MT translation re-train post-edit human translation
38 Incremental Updating 37 source text translate MT engine MT translation re-train post-edit human translation
39 Updatable Translation Table 38 Required: quickly add a sentence pair to the translation table word alignment phrase extraction phrase table building Online word alignment use existing model to align new sentence pair Online phrase table building Store parallel corpus in memory Index corpus with suffix array Extract phrases on the fly This can be done in less than 1 second
40 Suffixes 39 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 3 the people, by the people, for the people 4 people, by the people, for the people 5, by the people, for the people 6 by the people, for the people 7 the people, for the people 8 people, for the people 9, for the people 10 for the people 11 the people 12 people
41 Sorted Suffixes 40 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people
42 Suffix Array 41 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people suffix array: sorted index of corpus positions
43 Querying the Suffix Array 42 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people Query: people
44 Querying the Suffix Array 43 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people Query: people Binary search: start in the middle
45 Querying the Suffix Array 44 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people Query: people Binary search: discard upper half
46 Querying the Suffix Array 45 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people Query: people Binary search: middle of remaining space
47 Querying the Suffix Array 46 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people Query: people Binary search: match
48 Querying the Suffix Array 47 5, by the people, for the people 9, for the people 6 by the people, for the people 10 for the people 1 government of the people, by the people, for the people 2 of the people, by the people, for the people 12 people 4 people, by the people, for the people 8 people, for the people 11 the people 3 the people, by the people, for the people 7 the people, for the people Query: people Finding matching range with additional binary searches for start and end
49 Estimating Probablities 48 Phrase translation probability p(ē f) = count(ē, f) count( f) Suffix array allows quick estimation of count( f) By looping through the f, the count(ē, f) can be collected as well If there are too many f, we can resort to sampling
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