The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT Jorg¨ Tiedemann University of Helsinki
[email protected] https://github.com/Helsinki-NLP/Tatoeba-Challenge Abstract most important point is to get away from artificial This paper describes the development of a new setups that only simulate low-resource scenarios or benchmark for machine translation that pro- zero-shot translations. A lot of research is tested vides training and test data for thousands of with multi-parallel data sets and high resource lan- language pairs covering over 500 languages guages using data sets such as WIT3 (Cettolo et al., and tools for creating state-of-the-art transla- 2012) or Europarl (Koehn, 2005) simply reducing tion models from that collection. The main or taking away one language pair for arguing about goal is to trigger the development of open the capabilities of learning translation with little or translation tools and models with a much without explicit training data for the language pair broader coverage of the World’s languages. Using the package it is possible to work on in question (see, e.g., Firat et al.(2016a,b); Ha et al. realistic low-resource scenarios avoiding arti- (2016); Lakew et al.(2018)). Such a setup is, how- ficially reduced setups that are common when ever, not realistic and most probably over-estimates demonstrating zero-shot or few-shot learning. the ability of transfer learning making claims that For the first time, this package provides a do not necessarily carry over towards real-world comprehensive collection of diverse data sets tasks.