End-to-end statistical machine translation with zero or small parallel texts. (15th June 2016)

• Record Type: Journal Article
• Authors: IRVINE, ANN; CALLISON-BURCH, CHRIS
• Editors: Rapp, Reinhard; Sharoff, Serge; Zweigenbaum, Pierre
• Is Part Of: Natural language engineering. Volume 22:Part 4(2016)
• Page Start: 517
• Page End: 548
• Publication Date: 2016-06-15
• Subjects: Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324916000127
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: 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.