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Computer Aided Translation Philipp Koehn 30 April 2015 Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015 Why Machine Translation? 1 Assimilation reader initiates translation, wants to know content user is

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Computer Aided Translation

Philipp Koehn 30 April 2015

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Why Machine Translation?

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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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OUR FOCUS

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Why Machine Translation?

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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Goal: Helping Human Translators If you can’t beat them, join them.

→ How can machine translation help human translators?

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Post-Editing Machine Translation

(source: Autodesk)

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Machine Translation Quality Matters

Experiment: Post-editing with different machine translation systems English–German, news stories 5.38 sec/word

UEDIN SYNTAX

5.46 sec/word

ONLINE B

5.45 sec/word

UEDIN PHRASE

6.35 sec/word

OTHER WMT13

0 sec/word 2 sec/word 4 sec/word 6 sec/word [Koehn and Germann, 2014]

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Overview

  • Interactivity
  • Choices
  • Confidence
  • Adaptation

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactivity

  • Traditional professional translation approaches

– translation from scratch – post-editing translation memory match – post-editing machine translation output

  • More interactive collaboration between machine and professional?

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactive Machine Translation

Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator |

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactive Machine Translation

Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator | He

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactive Machine Translation

Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He | has

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactive Machine Translation

Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He has | for months

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactive Machine Translation

Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned |

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Interactive Machine Translation

Input Sentence Er hat seit Monaten geplant, im April einen Vortrag in Baltimore zu halten. Professional Translator He planned | for months

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Prediction from Search Graph

he it has planned has for since for months months months

Search for best translation creates a graph of possible translations

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Prediction from Search Graph

he it has planned has for since for months months months

One path in the graph is the best (according to the model) This path is suggested to the user

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Prediction from Search Graph

he it has planned has for since for months months months

The user may enter a different translation for the first words We have to find it in the graph

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Prediction from Search Graph

he it has planned has for since for months months months

We can predict the optimal completion (according to the model)

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Run Time

prefix time 5 10 15 20 25 30 35 40 0ms 8ms 16ms 24ms 32ms 40ms 48ms 56ms 64ms 72ms 80ms 0 edits 1 edit 2 edits 3 edits 4 edits 5 edits 6 edits 7 edits 8 edits Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Word Alignment Visualization

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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Word Alignment Visualization

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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Shading off Translated Material

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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Some Observations

  • 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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Overview

  • Interactivity
  • Choices
  • Confidence
  • Adaptation

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Choices

  • Trigger the passive vocabulary
  • Display multiple translations for words and phrases

er hat seit Monaten geplant , im M¨ arz 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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Alternative Translations

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.

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Bilingual Concordancer

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Overview

  • Interactivity
  • Choices
  • Confidence
  • Adaptation

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Confidence

  • 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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Sentence-Level Confidence

  • 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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Word-Level Confidence

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.

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Word-Level Confidence

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?)

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Automatic Reviewing

  • 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.

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Overview

  • Interactivity
  • Choices
  • Confidence
  • Adaptation

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Adaptation

  • 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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Incremental Updating

source text MT translation human translation MT engine post-edit re-train translate

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Incremental Updating

source text MT translation human translation MT engine post-edit re-train translate

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Incremental Updating

source text MT translation human translation MT engine post-edit re-train translate

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Updatable Translation Table

  • 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

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Suffixes

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 12 people 11 the people

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Sorted Suffixes

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

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Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

suffix array: sorted index of corpus positions

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Querying the Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

Query: people

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015

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Querying the Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

Query: people Binary search: start in the middle

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Querying the Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

Query: people Binary search: discard upper half

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Querying the Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

Query: people Binary search: middle of remaining space

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Querying the Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

Query: people Binary search: match

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Querying the Suffix Array

11 the people 6 by the people , for the people 10 for the people 5 , by the people , for the people 9 , for the people 1 government of the people , by the people , for the people 12 people 8 people , for the people 4 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 7 the people , for the people

Query: people Finding matching range with additional binary searches for start and end

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Estimating Probablities

  • Phrase translation probability

p(¯ e| ¯ f) = count(¯ e, ¯ f) count( ¯ f)

  • Suffix array allows quick estimation of count( ¯

f)

  • By looping through the ¯

f, the count(¯ e, ¯ f) can be collected as well

  • If there are too many ¯

f, we can resort to sampling

Philipp Koehn Machine Translation: Computer Aided Translation 30 April 2015