Name: opus100
Description: 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.
Provider: University of Edinburgh
Paper Title: Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
Tasks: machine translation 
Venue Name: nan
Authors: Biao Zhang, Philip Williams, Ivan Titov, Rico Sennrich
Abstract: 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