Modeling Target-Side Inflection in Neural Machine Translation
Table 8: English–German: lowercased BLEU for newstest’16 (2169 sentences) trained on 250k and 500k sentences news-mix data.
['[EMPTY]', '[BOLD] baseline', '[BOLD] morph-gen', '[BOLD] morph-gen-split']
To assess the influence of domain and corpus size, we also evaluate the approach to model German morphology in a larger news corpus setting. To obtain a training corpus that is diverse, but still restricted in size, we combined randomly selected sentences (between 5-50 words) from the 4 parallel corpora provided for EN–DE translation at the WMT’17 As for the TED data set, the morphological generation systems outperforms the systems trained on surface data, but the improvement for the system trained on 500k sentences is slightly lower than for the system trained on 250k sentences. The systems with additional compound splitting obtained the same result as the basis morphological generation system (250k), or were slightly better (500k). With regard to the effectiveness of compound handling, it is difficult to draw a clear conclusion, but, looking also at the results obtained in the TED setting, it seems that there is a tendency that compound handling leads to a slight improvement. As compounding is a productive word formation process that is challenging to cover even in large corpora, compound handling might be useful also when using larger data training corpora.