Difference between revisions of "Evaluation"
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Evaluation can give you some idea as to how well a language pair works in practice. There are many ways to evaluate, and the test chosen should depend on the intended use of the language pair: |
Evaluation can give you some idea as to how well a language pair works in practice. There are many ways to evaluate, and the test chosen should depend on the intended use of the language pair: |
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* how many words need to be changed before a text is publication-ready (Word-Error Rate, see [http://en.wikipedia.org/wiki/Word_error_rate Wikipedia on WER]) |
* how many words need to be changed before a text is publication-ready (Word-Error Rate, see [http://en.wikipedia.org/wiki/Word_error_rate Wikipedia on WER]), here '''lower scores are better''' |
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* how many [[N-gram]]'s are common to the MT output and one or more reference translations (see [http://en.wikipedia.org/wiki/BLEU Wikipedia on Bleu] or [http://en.wikipedia.org/wiki/NIST_%28metric%29 NIST]) |
* how many [[N-gram]]'s are common to the MT output and one or more reference translations (see [http://en.wikipedia.org/wiki/BLEU Wikipedia on Bleu] or [http://en.wikipedia.org/wiki/NIST_%28metric%29 NIST]), here '''higher scores are better''' |
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* how well a user ''understands'' the message of the original text (this typically requires an experiment with real human subjects). |
* how well a user ''understands'' the message of the original text (this typically requires an experiment with real human subjects). |
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Revision as of 12:52, 7 June 2010
Evaluation can give you some idea as to how well a language pair works in practice. There are many ways to evaluate, and the test chosen should depend on the intended use of the language pair:
- how many words need to be changed before a text is publication-ready (Word-Error Rate, see Wikipedia on WER), here lower scores are better
- how many N-gram's are common to the MT output and one or more reference translations (see Wikipedia on Bleu or NIST), here higher scores are better
- how well a user understands the message of the original text (this typically requires an experiment with real human subjects).
Using apertium-eval-translator for WER and PER
apertium-eval-translator is a script written in Perl. It calculates the word error rate (WER) and the position-independent word error rate (PER) between a translation performed by an Apertium-based MT system and its human-corrected translation at document level. Although it has been designed to evaluate Apertium-based systems, it can be easily adapted to evaluate other MT systems.
Usage
apertium-eval-translator -test testfile -ref reffile [-beam <n>] Options: -test|-t Specify the file with the translation to evaluate -ref|-r Specify the file with the reference translation -beam|-b Perform a beam search by looking only to the <n> previous and <n> posterior neigboring words (optional parameter to make the evaluation much faster) -help|-h Show this help message -version|-v Show version information and exit Note: The <n> value provided with -beam is language-pair dependent. The closer the languages involved are, the lesser <n> can be without affecting the evaluation results. This parameter only affects the WER evaluation. Note: Reference translation MUST have no unknown-word marks, even if they are free rides. This software calculates (at document level) the word error rate (WER) and the postion-independent word error rate (PER) between a translation performed by the Apertium MT system and a reference translation obtained by post-editing the system ouput. It is assumed that unknow words are marked with a start (*), as Apertium does; nevertheless, it can be easily adapted to evaluate other MT systems that do not mark unknown words with a star.
See English and Esperanto/Evaluation for an example. In Northern Sámi and Norwegian there is a Makefile to translate a set of source-language files and then run the evaluation on them.
Evaluating with Wikipedia
- Main article: Evaluating with Wikipedia