Identifying Infrequent Translations by Aligning Non Parallel Sentences.
Julien Bourdaillet, Philippe Langlais · Conference of the Association for Machine Translation in the Americas · 2012
Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We show that using only a few pairs of non parallel sentences allows to improve significantly the alignment of infrequent translations.