This presentation analyses the translation quality of MT systems for German to English using automatic evaluation metrics. I reported the results of my experiments with phrase based statistical machine translation using Moses. Nine setups were considered including the baseline and tuning process. The best setup takes advantage of the tuning and showed an improvement of translation quality in terms of BLEU. A brief introduction to factored MT system was taking into account in the last experiments in order to use the postag like a strategy in the target language (English).

1. Internal Evaluation German to English translation [email_address] , [email_address] Nervo Verdezoto D.

11. Baseline Table 1. Statistics of the Dataset de: wiederaufnahme der sitzungsperiode en: resumption of the session de: ich erkläre die am freitag , dem 17. dezember unterbrochene sitzungsperiode des europäischen parlaments für wiederaufgenommen , wünsche ihnen nochmals en: i declare resumed the session of the european parliament adjourned on friday 17 december 1999 , and i would like once again to wish you a happy new year in de: alles gute zum jahreswechsel und hoffe , daß sie schöne ferien hatten . en: the hope that you enjoyed a pleasant festive period . Figure 1. Sample of the training corpus German < >English Training Sentences 78524 Words 1581042 1684639 Dev Sentences 2000 Words 55118 58761 Test Sentences 2000 Words 55580 59153

14. Experiments Setup Description Setup 1, 2 Filter sentences Baseline - 40 Setup 1 – 45 Setup 2 -35 Setup 3,4 and 5 Combination with baseline, setup1 and setup2 and lexicalized reordering model (reordering configuration msd-bidirectional-fe and distorsion limit 6 ). Setup 3 – filter(40) Setup 4 –filter (45) Setup 5 –filter (35) Setup 6 I tried to split source data but it does not work Setup 7 and 8 Adding Part Of Speech information using Factored translation mode in the target data (English) / LM: Setup 7 (3gram), Setup 8(5gram) Setup 9 I tried to used Moses for factored translation model in the source (German) but it does not work. I tried to train the suppertagger with a German corpus (TIGGER corpus) and I got a problem with the format of the files see http://www.ims.uni-stuttgart.de/projekte/TIGER/TIGERCorpus/annotation

19. SETUP7

21. Experimental results Measure Baseline 40 SETUP 1 - 45 SETUP 2 35 BLUE 23.24% 23.20% 22.88% NIST 6.5426 6.5490 6.4742 WER 69.09% 69.08% 69.58% PER 18.82% 18.80% 18.84% Measure SETUP 3 40 SETUP 4 45 SETUP 5 35 BLUE 23.03% 23.06% 22.56% NIST 6.5168 6.5349 6.4485 WER 68.52% 68.29% 69.07% PER 19.46% 19.72% 19.46% Measure SETUP 7 40 - 3gram SETUP 8 40-5gram SETUP9 BLUE 21.51% 21.59% / NIST 6.1699 6.1754 / WER 73.29% 73.45% / PER 17.74% 17.39% /

22. MODEL EXAMPLE REFERENCE he wanted the presidency to outline the way forward at nice . BASELINE he HAS EXPRESSED THE WISH THAT the presidency IN NICE the way *** AHEAD AUFZEIGT TUNING he HAS EXPRESSED THE WISH THAT the presidency IN NICE the way *** AHEAD AUFZEIGT SETUP1 he HAS EXPRESSED THE WISH THAT the presidency IN NICE the way *** *** AHEAD SETUP2 he HAS EXPRESSED the WISH THAT THE PRESIDENCY IN NICE way AUFZEIGT THE FUTURE SETUP3 he HAS EXPRESSED THE WISH THAT the presidency IN NICE , the way *** AHEAD AUFZEIGT SETUP4 he HAS EXPRESSED THE WISH THAT the presidency IN NICE the way *** *** AHEAD SETUP5 he HAS EXPRESSED THE WISH THAT the presidency IN NICE , THE FUTURE PATH AUFZEIGT SETUP6 -- SETUP7 he HAS EXPRESSED THE WISH THAT the presidency IN NICE , THE FUTURE PATH SHOWS SETUP8 he HAS EXPRESSED THE WISH THAT the presidency IN NICE , the way *** AHEAD SHOWS