Multilingual Neural Machine Translation for Zero-Resource Languages

Understand

In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT).

• However, NMT systems are limited in translating low-resourced languages, due to the significant amount of parallel data that is required to learn useful mappings between languages.
• In this work, we show how the so-called multilingual NMT can help to tackle the challenges associated with low-resourced language translation.
• The underlying principle of multilingual NMT is to force the creation of hidden representations of words in a shared semantic space across multiple languages, thus enabling a positive parameter transfer across languages.