After the generation I had the following for corpora: Not modified data in DE-EN and CS-EN and Modified data in DE-EN and CS-EN.
In the folllowing I will refer them as Clean-de-en, Clean-cs-en, Tagged-de-en and Tagged-cs-en.
-
-
-## Training and Model Selection
-### Training
-I trained an bidirectional LSTM with the configuration shown \TODO{add config}.
-I ran a hyperparameter optimization on the optimzier (sgd, adam, adadelta), the learning rate(1, 0.1, 0,001, 0001) and the beginning of the learning rate decay(off, 5 epochs, 10 epochs).
-The OpenNMT framework was used to train the models and translate the scoring tests.
-In the OpenNMT context one epoch of the corpus translates roughly to 2,000 train steps.
-All models were trained for 18 epochs.
-I used a small MQTT scheduler to coordinate the runs on a mixture of NVIDIA GTX 980, 1080 and 1080Ti.
-One run takes between 2 and 3,5 hours depending on the GPU.
-Most of the models showed a decent trainings curve \TODO{image good curve} as this example run caped after ~5epochs.
-Some hyper parameter configuration learned comparable slow \TODO{image bad curve} and some configurations like \todo{show super mad config in table} produced unusable results.
-
-All models were trained multiple times to ensure proper distribution of start vectors.
-
-### Selection of Hyper Parameter
-The resulting models were used to translate 1,000 examples from the validation data.
-The generated translation were stripped from the BPE and scored using BLEU \TODO{fancy optim}.
-From the ranking \TODO{include table} the best model were choosen for each corpus.
-
-## Scoring and Comparison
-The figure \TODO{side constraint comparison} shows the performance of the best model for the domain test sets calculated with bleu.
-As expected the related language pair had archived better scores overall domains.
-
-\TODO{write something about the different domains}
-
-However depending on the scoring metric, there were huge differences in the actual performance change between the pairs.
-The models that were trained with prefix constraints archieved a higher trainings and validation accuracy, however only in the CZ-EN pair.
-In the DE-EN pair was not noticeable difference in the trainings statistic.
-
-For more content focused scores like BLEU or ROUGE \TODO{add table with socres} the prefix constraints impacted the score slightly negative.
-
-While the difference is pretty small for related languages, it is notable different for the distant language pair.
--- /dev/null
+## Training and Model Selection
+### Training
+I trained an bidirectional LSTM with the configuration shown \TODO{add config}.
+I ran a hyperparameter optimization on the optimzier (sgd, adam, adadelta), the learning rate(1, 0.1, 0,001, 0001) and the beginning of the learning rate decay(off, 5 epochs, 10 epochs).
+The OpenNMT framework was used to train the models and translate the scoring tests.
+In the OpenNMT context one epoch of the corpus translates roughly to 2,000 train steps.
+All models were trained for 18 epochs.
+I used a small MQTT scheduler to coordinate the runs on a mixture of NVIDIA GTX 980, 1080 and 1080Ti.
+One run takes between 2 and 3,5 hours depending on the GPU.
+Most of the models showed a decent trainings curve \TODO{image good curve} as this example run caped after ~5epochs.
+Some hyper parameter configuration learned comparable slow \TODO{image bad curve} and some configurations like \todo{show super mad config in table} produced unusable results.
+
+All models were trained multiple times to ensure proper distribution of start vectors.
+
+### Selection of Hyper Parameter
+The resulting models were used to translate 1,000 examples from the validation data.
+The generated translation were stripped from the BPE and scored using BLEU \TODO{fancy optim}.
+From the ranking \TODO{include table} the best model were choosen for each corpus.
+
+## Scoring and Comparison
+The figure \TODO{side constraint comparison} shows the performance of the best model for the domain test sets calculated with bleu.
+As expected the related language pair had archived better scores overall domains.
+
+\TODO{write something about the different domains}
+
+However depending on the scoring metric, there were huge differences in the actual performance change between the pairs.
+The models that were trained with prefix constraints archieved a higher trainings and validation accuracy, however only in the CZ-EN pair.
+In the DE-EN pair was not noticeable difference in the trainings statistic.
+
+For more content focused scores like BLEU or ROUGE \TODO{add table with socres} the prefix constraints impacted the score slightly negative.
+
+While the difference is pretty small for related languages, it is notable different for the distant language pair.