On integrating a language model into neural machine translation. (September 2017)

• Record Type: Journal Article
• Authors: Gulcehre, Caglar; Firat, Orhan; Xu, Kelvin; Cho, Kyunghyun; Bengio, Yoshua
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 137
• Page End: 148
• Publication Date: 2017-09
• Subjects: Neural machine translation -- Monolingual data -- Language models -- Low resource machine translation -- Deep learning -- Neural network; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.01.014
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Recent advances in end-to-end neural machine translation models have achieved promising results on high-resource language pairs such as En→ Fr and En→ De. One of the major factor behind these successes is the availability of high quality parallel corpora. We explore two strategies on leveraging abundant amount of monolingual data for neural machine translation. We observe improvements by both combining scores from neural language model trained only on target monolingual data with neural machine translation model and fusing hidden-states of these two models. We obtain up to 2 BLEU improvement over hierarchical and phrase-based baseline on low-resource language pair, Turkish→ English. Our method was initially motivated towards tasks with less parallel data, but we also show that it extends to high resource languages such as Cs→ En and De→ En translation tasks, where we obtain 0.39 and 0.47 BLEU improvements over the neural machine translation baselines, respectively.