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Shared Private Bilingual Word Embeddings for Neural Machine Translation

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Academic year: 2020

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Figure 1: Comparison between (a) standard word em-beddings and (b) shared-private word embeddings
Figure 2: Shared-private bilingual word embeddings perform between the source and target words or sub-words(a) with similar lexical meaning, (b) with same word form, and (c) without any relationship
Figure 3: The example of assembling the source word embedding matrix. The words in parentheses denote thepaired words sharing features with them.
Table 1: Results on the NIST Chinese-English translation task. “Params” denotes the number of model parameters.“Emb.” represents the number of parameters used for word representation
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