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Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks

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Academic year: 2020

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Figure 1: Fragment of English sentence and its in-correct Russian translation produced by the base-line SMT system
Figure 2: Feed-forward BNN architectures for predicting target translations: (a) word model (similar tostem model), and (b) suffix model with an additional vector representation r σ for target stems σ .
Table 2: BNN training corpora statistics: numberof sentences, tokens, and type/token ratio (T/T).
Figure 4:Effect of different word segmentationtechniques (U: unsupervised, S: supervised, R:rule-based stemmer) on stem and suffix predictionaccuracy
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