opus100
OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.
University of Edinburgh
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
machine translation 
nan
Biao Zhang, Philip Williams, Ivan Titov, Rico Sennrich
Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations.
In this paper, we explore ways to improve
them. We argue that multilingual NMT requires stronger modeling capacity to support
language pairs with varying typological characteristics, and overcome this bottleneck via
language-specific components and deepening
NMT architectures. We identify the off-target
translation issue (i.e. translating into a wrong
target language) as the major source of the
inferior zero-shot performance, and propose
random online backtranslation to enforce the
translation of unseen training language pairs.
Experiments on OPUS-100 (a novel multilingual dataset with 100 languages) show that
our approach substantially narrows the performance gap with bilingual models in both oneto-many and many-to-many settings, and improves zero-shot performance by ∼10 BLEU,
approaching conventional pivot-based methods