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Addressing the Rare Word Problem in Neural Machine Translation

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Academic year: 2020

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Table 1: Tokenized BLEU on newstest2014terms of: (a) the architecture, (b) the size of the vocabulary used, and (c) the training corpus, eitherusing the full WMT’14 corpus of 36M sentence pairs or a subset of it with 12M pairs
Figure 5: Rare word translation – On the x-axis,we order newstest2014 sentences by their aver-age frequency rank and divide the sentences intogroups of sentences with a comparable prevalenceof rare words
Figure 8: Perplexity vs. BLEUcorrelation by evaluating an LSTM model with 4 – we show thelayers at various stages of training.
Table 3: Sample translationsbefore ( – the table shows the source (src) and the translations of our best modeltrans) and after (+unk) unknown word translations

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