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Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation

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arxiv 1909.00437 v1 pith:Y7NZMU5A submitted 2019-09-01 cs.CL

Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation

Aditya Siddhant , Melvin Johnson , Henry Tsai , Naveen Arivazhagan , Jason Riesa , Ankur Bapna , Orhan Firat , Karthik Raman This is my paper
classification cs.CL
keywords cross-lingualmultilinguallanguagesmassivelytaskstransfertranslationdownstream
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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