We use bilingual lexicon induction techniques, which learn translations from monolingual texts in two languages, to build an end-to-end statistical machine translation (SMT) system without the use of any bilingual sentence-aligned parallel corpora. We present detailed analysis of the accuracy of bilingual lexicon induction, and show how a discriminative model can be used to combine various signals of translation equivalence (like contextual similarity, temporal similarity, orthographic similarity and topic similarity). Our discriminative model produces higher accuracy translations than previous bilingual lexicon induction techniques. We reuse these signals of translation equivalence as features on a phrase-based SMT system. These monolingually-estimated features enhance low resource SMT systems in addition to allowing end-to-end machine translation without parallel corpora. Natural Language Engineering 1 (1): 1-34. Printed in the United Kingdom
REF show that combining monolingual signals in a supervised logistic regression model produces higher accuracy word translations than unsupervised models.
End-to-end statistical machine translation with zero or small parallel texts
Nat. Lang. Eng.
Nat. Lang. Eng.
Computer Science