Multilingual Neural Machine Translation for Low Resource Languages

Abstract

Neural Machine Translation (NMT) has been shown to be more effective in translation tasks compared to the Phrase-Based Statistical Machine Translation (PBMT). However, NMT systems are limited in translating low-resource languages (LRL), due to the fact that neural methods require a large amount of parallel data to learn effective mappings between languages. In this work we show how so-called multilingual NMT can help to tackle the challenges associated with LRL translation. Multilingual NMT forces words and sub-words representation in a shared semantic space across multiple languages. This allows the model to utilize a positive parameter transfer between different languages, without changing the standard attentionbased encoder-decoder architecture and training modality. We run preliminary experiments with three languages (English, Italian, Romanian) covering six translation directions and show that for all available directions the multilingual approach, i.e. just one system covering all directions is comparable or even outperforms the single bilingual systems. Finally, our approach achieve competitive results also for language pairs not seen at training time using a pivoting (x-step) translation.