Semi-Supervised Learning for Bilingual Lexicon Induction
Paul Garnier Gauthier Guinet
We consider the problem of aligning two sets of continuous word
representations, corresponding to languages, to a common space in order to
infer a bilingual lexicon. It was recently shown that it is possible to infer
such lexicon, without using any parallel data, by aligning word embeddings
trained on monolingual data. Such line of work is called unsupervised bilingual
induction. By wondering whether it was possible to gain experience in the
progressive learning of several languages, we asked ourselves to what extent we
could integrate the knowledge of a given set of languages when learning a new
one, without having parallel data for the latter. In other words, while keeping
the core problem of unsupervised learning in the latest step, we allowed the
access to other corpora of idioms, hence the name semi-supervised. This led us
to propose a novel formulation, considering the lexicon induction as a ranking
problem for which we used recent tools of this machine learning field. Our
experiments on standard benchmarks, inferring dictionary from English to more
than 20 languages, show that our approach consistently outperforms existing
state of the art benchmark. In addition, we deduce from this new scenario
several relevant conclusions allowing a better understanding of the alignment
phenomenon.