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Poster
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
Junjie Hu · Sebastian Ruder · Aditya Siddhant · Graham Neubig · Orhan Firat · Melvin Johnson

Tue Jul 14 10:00 AM -- 10:45 AM & Tue Jul 14 09:00 PM -- 09:45 PM (PDT) @
in Poster Session 4 »

Much recent progress in applications of machine learning models to NLP has been driven by benchmarks that evaluate models across a wide variety of tasks. However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing. To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models, particularly on syntactic and sentence retrieval tasks. There is also a wide spread of results across languages. We will release the benchmark to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks.

Keywords: Transfer and Multitask Learning · Natural Language Processing / Dialogue · Applications - Language, Speech and Dialog

Author Information

Junjie Hu (Carnegie Mellon University)
Sebastian Ruder (DeepMind)
Aditya Siddhant (Google Research)
Graham Neubig (Carnegie Mellon University)
Orhan Firat (Google)
Melvin Johnson (Google)

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