* !! Under construction !! *

Hanna Béchara, Yanjun Ma, Josef van Genabith
MT Summit 2011

Presented by Rudolf Rosa
Report by Jindřich Helcl

This article was about statistical post-editing on results of a statistical machine translation system. The most interesting part on this article was that authors claim that they achieved improvement of about 2 BLEU score points by pipelining two statistical MT systems, which was until then considered useless.

The paper frequently quotes another article from Simard et al. (2007), which has been also briefly presented in the beginning of the presentation and which you can read online here.

A brief outline of the paper follows. In introduction, previous work has been briefly presented, it was stated that any results of this method were either none or not statistically significant.

• Data: The data for the experiment came from English-French translation memory from Symantec. The size of the data was about 55k sentences (0.8M words) in each language. In the paper, they call the English training data E and the French data F.
• Architecture: They wanted to train the same system to do the translation and post-editing. To overcome training on the same data, they build a third dataset F' using 10-fold cross validation approach on resutls of the first translation system trained on datasets E and F. After that, they trained the second system on datasets F' and F to learn it “translate” from (French) results of the first system to the “real-world” French.
• Enhancements: However, the basic architecture of this system did not produce any improvements. There was a drop of 0.15 BLEU points against the baseline without post-editing in English-to-French translation and only 0.65 BLEU points increase in French-to-English. So they introduced following enhancements:
  • Contextual SPE, which means that the translated words was created by concatenating the English word and the translation separated by hash sign to one resulting word. This new dataset is called E#F' in the paper. With this enhancement, they were able to do post-editing of translated text with regard to original text.
  • Next, they striped off the #-postfixes of non-translated words.
  • Then, they do alignment between the source text and the translation and use the contextual enhancement only where the alignment weight was over some threshold.

    With the last enhancement, they achieve improvement of 2 BLEU points in French-English translation.

Following topics about the article were discussed on RG meeting:

• As the main possible flaw of the experiment was assumed the size of the data (only 55k sentences). On the other hand, the data from translation memory were mentioned to be clean and there were not duplicities. However, the authors do not explain why they took so small data when other options are easily available. One possible explanation is that their translation system was built for the domain from the Symantec data - but this is not explicitly said in the article.
• In the paper, they state that they use 10-fold cross validation approach to build a new dataset. Many of us have got confuset by this statement and found unclear what exactly the authors meant by this. We finally agreed that the new dataset is created fold-by-fold by training the SMT on the other 9 folds of E and F and then running it on the tenth fold of source language.
• #
• alignment
• struktura článku

- zhodnocení