applicative evaluation of bilingual terminologies

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Applicative evaluation of bilingual terminologies

Estelle DelpechNODALIDA 12th May 2011

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Outline

1. Context and scope of work2. Comparable corpora and terminology evaluation3. Applicative evaluation protocol4. Experimentation and results5. Future improvements

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Outline


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Context of the work

• Bilingual terminology mining from comparable corpora

• Application to: – computer-aided translation– computer-aided terminology

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Scope of the work

• Find a way to show the "added-value" of the acquired terminology when used for technical translation– do translators translate better and/or faster ?

• Conception and experimentation of an "applicative" evaluation protocol for bilingual terminologies

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Outline


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Comparable corpora

English texts on breast cancer

French texts on breast cancer

It has been suggested that breast magnetic resonance imaging (MRI) is more accurate in the diagnosis of breast cancer...

Histological evaluation revealed the presence of DCIS...

L'imagerie par résonance magnétique avec injection de gadolinium (IRM) est une technique indépendante de la densité mammaire....

Un diagnostic histologique est nécessaire...

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Comparable corpora

English texts on breast cancer

French texts on breast cancer


Histological evaluation revealed the presence of DCIS...



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Comparable corpora

English texts on breast cancer French texts on breast cancer


Histological evaluation revealed the presence of ductal carcinoma in situ.



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Advantages of comparable corpora

• More available– new domains– unprecedented language pairs

• Quality– spontaneous language– not influenced from source texts

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Reference evaluation of bilingual terminologies

• Reference evaluation: – output of the program is compared with a list

of reference translations• Precision:

– percentage of output translations which are in the reference

output∩referenceoutput

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Reference evaluation with comparable corpora

• Output:– source term → ordered list of candidate

translations• Example:

– histological → diagnostic1, histologie2,

histologique3, … nécessairen

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Reference evaluation with comparable corpora

• Precision: – percentage of output translations which are in

the reference when you take into account the Top 20 or Top 10 candidate translations

• State-of-the-art:– between 42% and 80% on Top 20

depending on corpus size, corpus type, nature of translated elements [Morin and Daille, 2009]

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Reference vs. Applicative evaluation

• Reference evaluation: – ok for testing/developing the alignment

program– fast, cheap, reproducible, objective

• Applicative evaluation:– how much does the alignment program help

the end-users ?– can the terminologies improve translation

quality?

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Outline


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Applicative evaluation scenario

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Questions raised

2) Evaluate the whole of the translations or technical terms only ?

1) How do you assess translation quality ?

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• Translation studies evaluation grids:– SICAL, SAE J 2450– too complex, scarcely documented

• Machine translation objective metrics – BLEU, METEOR– not adapted to human translation– reproducibility is not an advantage in our case

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• Machine translation subjective evaluation– translations evaluated by humans:

• quality judgement: adequacy, fluency... • ranking

– use annotator agreement measure to ensure judges agreement is sufficient

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2) Evaluate the whole text or just some terms ?

• Quality of a text translation = complex interaction of several parameters

• Focus on those elements for which the translator felt he/she needed a linguistic resource:– evaluates only the part of the translation on

which the terminology has an impact– easier and faster

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Applicative evaluation protocol

• Compare 3 different "situations of translations" – one situation = one type of resource

• Translators do the translation, note down the terms they had to look up

• The quality of the terms' translations is assessed by human judges

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Situations of translation

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Translations' assessment

1. Quality judgement : – correct: standard term or expression– acceptable: meaning is retained– wrong: no meaning is retained

2. Ranking : – from best to worst– ties allowed

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Outline


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Data

• Comparable corpora : – breast cancer: 400k words/language– water science: 2M words/language

• Texts to translate :– research paper abstracts: ~500 words/domain– lay science texts: ~500 words/domain

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Translators' feedback

" Globally, 75% of technical words aren't in the glossary, and for the other 25%, 99% have between 10 and 20 candidate translations and none has been validated. So most of the time, you are just partly sure, but you are never totally sure of your translation. And in the worst cases, you translate instinctively ".

Translators were not prepared to use a bilingual terminology with many candidate translations The terminology covered partially the vocabulary of the texts to translate

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Terminology coverage of texts to translate

• Breast Cancer – 94% of the vocabulary of the texts is in the

terminology – fine-grained topic

• Water Science– 14% of the vocabulary of the texts is in the

terminology– topic is too general

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Quality judgement / Breast Cancer

SIT. 0 / GEN. LANG.SIT. 1 / CC

SIT. 2 / WEB

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

38% 43% 47%

42% 38% 35%

20% 19% 18%

BREAST CANCERK = 0,25

• equivalent proportion of incorrect translations

• Internet gives the more correct translations, then the Comparable Corpora.

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Quality judgement / Water Science

• Translations are much better with Internet

• Comparable corpora produces worse translations than the general resources

SIT. 0/ GEN. LANG.SIT. 1 / CC

SIT. 2 / WEB

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

59% 56%77%

23% 23%

16%18% 21%

7%

WATER SCIENCEK = 0,42

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Results seem incoherent

• Translations produced in situation 1 are worse than translations produced in sit. 2

• But they share the same "general language resource" basis

BASELINE Situation 1

generallanguageresources

Terminologymined from

COMPARABLECORPORA

general languageresources

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Possible explanation

When translators have a specialized ressource they tend to ignore the general language resource

BASELINE SITUATION 1Comparable corpora

SITUATION 2Web

General Language resource 43% 14% 3%

Specialized resource - 25% 56%

Intuition 79% 77% 44%

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Possible explanation

If translators of situation 1 had always looked up the general resource first, translations of situation 1 would have been at least as good as translations of situation 0

BASELINESITUATION 1Comparable

corporaSITUATION 2

Web

General Language resource 43% 14% 3%

Specialized resource - 25% 56%

Intuition 79% 77% 44%

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Ranking / Breast Cancer

CC vs. GEN. LANG. CC vs. WEB0

5

10

15

20

25

30

35

40

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28% 26%

47% 42%

26%

32%

BREAST CANCERK=0,69

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Ranking / Water science

CC vs. GEN. LANG. CC vs. WEB0

10

20

30

40

50

60

70

80

90

18% 16%

49%41%

33%

43%

WATER SCIENCEK=0,63

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Outline

1. Context and scope of work2. Bilingual terminology mining : comparable vs. parallel corpora3. Evaluation of bilingual terminologies4. Applicative evaluation protocol5. Experimentation and results6. Future improvements

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Improvements: terminology coverage

• dependency between:– added-value of the bilingual terminology– its coverage of the texts to translate

• any added-value measure should also indicate to what extent the terminology contains the vocabulary of the translated texts

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Improvement 1: terminology coverage

• Perspectives:– create a "coverage" measure– find out what is the minimum coverage for a

terminology to be "useful" to translate a given text

– gather smaller but finer-grained corpora

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Improvement 2: situations of translations

• When translators have several ressources at their disposal, they tend to ignore the general language resource

• Consequence : the same resource is used differently depending on the situation

• Seems to be the cause for incoherent results

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Improvement 2: situations of translations

• Perspective : use 0 or 1 resource per situation of translation

Situation 0 Situation 1

terminologymined fromComparable

Corpora

Web

Situation 2

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Improvement 3: train translators

• Prepare translators to use "ambiguous", unvalidated terminologies

• Do a first blank evaluation to :– train the translators– train the judges → results in higher

agreement

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Acknowledgements

This work was funded by:– French National Research Agency, subvention

n° ANR-08-CORD-009– Lingua et Machina, www.lingua-et-machina.com

Annotators:– Clémence De Baudus– Mathieu Delage

applicative evaluation of bilingual terminologies

Technology

terminology evaluation

histological evaluation

translation quality

comparable corpora output

comparable corpora precision

comparable corpora application

breast cancerit

technical translation