Курсы машинного перевода для языков России/Session 7

Now that all of the basic aspects of creating a new MT system in Apertium have been covered, we come to the final, and possibly most important one. This session will cover the question of why we need data consistency, what we mean by quality and how to perform an evaluation. The practical will involve working with some of the methods that we use to assure consistency and quality in Apertium. It will also cover quality evaluation.

Theory

Consistency

Self-contained system

In contrast to many other types of systems for natural language processing — such as morphological analysers and part-of-speech taggers, a machine translation system designed and developed with Apertium is a self-contained system. For any input, it should have one, predictable, deterministic output.

Put another way, every lexical unit in the source language morphological analyser should have a corresponding entry in the transfer lexicon, and subsequently an entry in the morphological generator for the target language. Lexical units added in the transfer stage should also have entries in the target language generator. This must be on the level of both lemmas and tags. Consider the following example: we can see that for each source language lexical form, there is a corresponding entry in the bilingual dictionary.

When this is not the case, things go wrong, you get diagnostic symbols (@, #) in your output and your translation looks like it has been dragged through a hedge backwards. This is the principle difference between the different statūs of language pairs. We can see this clearly if we take a pair with trunk (nearly always for released pairs which have been quality controlled — that is they do not have diagnostic symbols) status, and a pair with nursery (for pairs which have only undergone basic development and are pending quality control) status.

Inconsistency

This is demonstrated if we try and translate the sentence in Turkish below with two distinct translators in Apertium:

Original (Turkish)	Trunk (Turkish → Kyrgyz)	Incubator (Turkish → Chuvash)
22 yaşındaki Çavuş Sandra Radovanoviç, Sırp Ordusu'nun Super Galep G4'ünü uçuran ilk kadın oldu.	22 жашындагы Сержант Сандра Радованович, Серб Аскеринин *Super Galep Г4үндү учкан биринчи аял болду.	#22 @yaş @Çavuş @Sandra @Radovanoviç#, @Sırp Ordusu *Super Galep *G4@'#чап @uç #пӗрремӗш #арӑм #пул#.
Yaşıtları daha araba kullanmayı yeni öğrenirken, Radovanoviç bir savaş uçağına 4.000m irtifada, 700km/sa hızla manevra yaptırıyor.	Теңтуштары дагы араба колдонууну эми үйрөнөт, Радованович бир согуш учагына *4.000m бийиктикте, *700km/*sa ылдамдык менен манёвр кылдырат.	#Тантӑш #ӗнтӗ #ӑйӑ @kullan @yeni @öğren @0#, @Radovanoviç #пӗр-пӗр #вӑрҫӑ @uçak #4.000 #ҫӳл#, *700km/*sa #хӑвӑртлӑх @manevra @yap#.
Radovanoviç, Sırbistan'ın ilk kadın pilotu olarak tarihe geçti.	Радованович, Сербиянын биринчи аял пилотту боло тарыхка өттү.	@Radovanoviç#, @Sırbistan #пӗрремӗш #арӑм #лётчик #пул #хисеп @geç#.
Pilot, Belgrad'daki harp okulu Havacılık Okulu'nda son sınıf öğrencisi olarak okuyor.	Пилот, Белграддагы согуш мектеби Абаачылык Мектеби'*nda акыркы класс окуучусу боло окуйт.	#Лётчик#, @Belgrad @harp #шкул #Авиаци #Шкул@'*nda @son #курс #студент #пул @oku#.
Üç yıl önce, hayatında ilk defa bir uçağa bindi.	Үч жыл мурда, жашоосунда биринчи жолу бир учакка минди.	#Виҫ #ҫул #ӗлӗк#, #чӗрлӗх #пӗрремӗш #рас #пӗр-пӗр @uçak @bin#.

The diagnostic symbols @ and # mark errors in the lexical transfer, and in the morphological transfer/generation respectively.

• A missing lemma from the bilingual dictionary will produce an @ followed by the missing lemma. For example: Belgrad → @Belgrad, the word Belgrad does not appear in the bilingual dictionary.
• If the lemma exists in the bilingual dictionary, but the lemma with the part-of-speech tag does not exist, an @ will also be produced. For example: uç → @uç, the word uç only appears with the tag <n>, but the tagger choses <v>.
• If the lemma and the part-of-speech tag exist in the bilingual dictionary, but some other tags do not match, an @ will be produced.
• If a lemma does not exist in the target language dictionary then a # will be produced. For example: kadın → арӑм → #арӑм
• If a lemma exists in the target language morphological dictionary, but there is a mismatch in the morphological tags between the output of transfer and the morphological dictionary, then a # will be produced. For example: ol → пул. The target dictionary does not have the form <past><p3><sg> for the lemma пул.

In principle, before any translator is released, a full test of the dictionaries must be performed (colloquially called testvoc — from test vocabulary), and no diagnostic symbols must be present in the translation. In practice, sometimes errors may remain.

One of the reasons that Apertium avoids modelling unrestricted derivational processes is because they may not be equivalent in both languages. If they are not equivalent, and the transfer rules are not in place, then debugging and testing the translator is much more difficult. Also, if the morphological transducers are cyclic (allow unrestricted derivation) it is impossible to perform a vocabulary test.

Evaluation

Vocabulary coverage

The coverage of a system is an indication of how much of the vocabulary it covers in a given corpus or domain. For an idea of what this means, we will try translating a sentence with different levels of coverage:

Sentence	Coverage
Селскостопанските отрасли в Косово и Македония ще получат тласък. Селскостопанските отрасли en Косово и Македония ще получат тласък.	11%
Селскостопанските отрасли в Косово и Македония ще получат тласък. Селскостопанските отрасли en Косово y Македония ще получат тласък.	22%
Селскостопанските отрасли в Косово и Македония ще получат тласък. Селскостопанските отрасли en Косово y Македония получат тласък.	33%
Селскостопанските отрасли в Косово и Македония ще получат тласък. Селскостопанските отрасли en Косово y Македония recibirá тласък.	44%
Селскостопанските отрасли в Косово и Македония ще получат тласък. El agrícola отрасли en Косово y Македония recibirá тласък.	55%
Селскостопанските отрасли в Косово и Македония ще получат тласък. El agrícola отрасли en Косово y Македония recibirá empujón.	66%
Селскостопанските отрасли в Косово и Македония ще получат тласък. El sector agrícola en Косово y Македония recibirá empujón.	77%
Селскостопанските отрасли в Косово и Македония ще получат тласък. El sector agrícola en Косово y Macedonia recibirá empujón.	88%
Селскостопанските отрасли в Косово и Македония ще получат тласък. El sector agrícola en Kosovo y Macedonia recibirá empujón.	100%

Usually, coverage is given over a set of sentences, or corpus, instead of over a single sentence. In Apertium, the baseline coverage for releasing a new prototype translator is around 80%, or 2 unknown words in 10 for a given corpus. This is not enough to make revision practical, except in the case of closely-related languages. However, it is usually enough to make translations which are intelligible.

Error rate

While the coverage gives you an idea of how many words you will have to change in the best case, that is, that the rest of the translation is correct. A more accurate indication of how many words you will have to change when using the translator is given by post-edition word error rate (often abbreviated as wer). This is given as a percentage of changes (insertions, deletions, substitutions) between a machine translated sentence, and a sentence which has been revised by a human translator.

Taking the example above:

		Changes	wer
Original	Селскостопанските отрасли в Косово и Македония ще получат тласък.	—
Machine translation	El sector agrícola en Kosovo y Macedonia recibirá empujón.	—
substitute	El sector agricultura en Kosovo y Macedonia recibirá impulso.	2/9
insert	El sector de la agricultura en Kosovo y Macedonia recibirá un impulso.	3/9
Revised	El sector de la agricultura en Kosovo y Macedonia recibirá un impulso.	5/9	55.56%

As with coverage, error rate evaluation is usually carried out on a corpus of sentences. So it gives you an indication of how many words you are likely to have to change in a given sentence.

When calculated over an appropriate corpus of the target translation domain, the combination of word error rate and coverage can give an idea of the usefulness of a machine translation system for a specific task. Of course, to determine if a system is useful for translators, a more thorough and case-specific evaluation needs to be made.

Practice

Quality

Testvoc

To get an idea of how to run a testvoc script, and what the output looks like, go to the apertium-tt-ba directory. Make sure the translator is compiled (e.g. type make) and then enter the dev/ subdirectory.

In order to perform the testvoc you need to run the testvoc.sh command:

$ sh testvoc.sh 

The whole test will take around 10—15 minutes to run, and will generate around 500M of output.

Evaluation

Word error rate

Apertium has a tool for calculating the Word error rate between a reference translation and a machine translation. The tool is called apertium-eval-translator and can be found in the trunk of the Apertium SVN repository. The objective of this practical is to try it out on the system you have created.

You will need two reference translations. The first will be the "original" text in the target language, this was created without post-editting. The second will be a post-editted version of the machine translation text. When you are creating the post-editted version, take care to make only the minimal changes required to produce an adequate translation.

Here is an example for Bashkir to Tatar. Presuming the example text is in a file called ba.txt, run the command:

$ cat ba.txt  | apertium -d . ba-tt > ba-tt.txt

Check that the file has been created properly, then run:

$ apertium-eval-translator -r tt.txt -t ba-tt.txt 
Test file: 'ba-tt.txt'
Reference file 'tt.txt'

Statistics about input files
-------------------------------------------------------
Number of words in reference: 311
Number of words in test: 313
Number of unknown words (marked with a star) in test: 
Percentage of unknown words: 0.00 %

Results when removing unknown-word marks (stars)
-------------------------------------------------------
Edit distance: 42
Word error rate (WER): 13.42 %
Number of position-independent word errors: 42
Position-independent word error rate (PER): 13.42 %

Results when unknown-word marks (stars) are not removed
-------------------------------------------------------
Edit distance: 42
Word Error Rate (WER): 13.42 %
Number of position-independent word errors: 42
Position-independent word error rate (PER): 13.42 %

Statistics about the translation of unknown words
-------------------------------------------------------
Number of unknown words which were free rides: 0
Percentage of unknown words that were free rides: 0%

This gives information about the Word error rate, and some other statistics about the two files. The -r argument gives the reference translation, in this case tt.txt is the file containing the example text in Tatar. The -t gives the test text, e.g. the output of the machine translation system.

Now make a copy of the file ba-tt.txt called tt2.txt and edit it so that it becomes an adequate translation, then rerun the above commands, substituting tt.txt with tt2.txt and compare the results.