Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation

Understand

Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT).

• However, these models still struggle in a variety of ways, including aspects of translation that for a human are the easiest - for instance, correctly translating common nouns.
• This work explores a cheap and abundant resource to combat this problem: bilingual lexica.
• We test the efficacy of bilingual lexica in a real-world set-up, on 200-language translation models trained on web-crawled text.