Word Alignment is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other.
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We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.
Ranked #2 on Word Alignment on fr-en
In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models.
Our experiments on the WMT14 English to French translation task show that this method provides a substantial improvement of up to 2. 8 BLEU points over an equivalent NMT system that does not use this technique.
Ranked #21 on Machine Translation on WMT2014 English-French
Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT.
Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP.