The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model. Its improved translation performance on low resource languages hints at potential cross-lingual transfer capability for downstream tasks. In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of a massively multilingual NMT model on 5 downstream classification and sequence labeling tasks covering a diverse set of over 50 languages. We compare against a strong baseline, multilingual BERT (mBERT), in different cross-lingual transfer learning scenarios and show gains in zero-shot transfer in 4 out of these 5 tasks.
@article{arxiv.1909.00437,
title = {Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation},
author = {Aditya Siddhant and Melvin Johnson and Henry Tsai and Naveen Arivazhagan and Jason Riesa and Ankur Bapna and Orhan Firat and Karthik Raman},
journal= {arXiv preprint arXiv:1909.00437},
year = {2019}
}