  In this paper, we propose a new task of machine translation (MT), which is
based on no parallel sentences but can refer to a ground-truth bilingual
dictionary. Motivated by the ability of a monolingual speaker learning to
translate via looking up the bilingual dictionary, we propose the task to see
how much potential an MT system can attain using the bilingual dictionary and
large scale monolingual corpora, while is independent on parallel sentences. We
propose anchored training (AT) to tackle the task. AT uses the bilingual
dictionary to establish anchoring points for closing the gap between source
language and target language. Experiments on various language pairs show that
our approaches are significantly better than various baselines, including
dictionary-based word-by-word translation, dictionary-supervised cross-lingual
word embedding transformation, and unsupervised MT. On distant language pairs
that are hard for unsupervised MT to perform well, AT performs remarkably
better, achieving performances comparable to supervised SMT trained on more
than 4M parallel sentences.
