mT5: A massively multilingual pre-trained text-to-text transformer

The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent "accidental translation" in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering DaNetQA MT5 Large Accuracy 0.657 # 10
Natural Language Inference LiDiRus MT5 Large MCC 0.061 # 16
Reading Comprehension MuSeRC MT5 Large Average F1 0.844 # 2
EM 0.543 # 4
Common Sense Reasoning PARus MT5 Large Accuracy 0.504 # 14
Natural Language Inference RCB MT5 Large Average F1 0.366 # 11
Accuracy 0.454 # 15
Common Sense Reasoning RuCoS MT5 Large Average F1 0.57 # 10
EM 0.562 # 10
Common Sense Reasoning RWSD MT5 Large Accuracy 0.669 # 8
Natural Language Inference TERRa MT5 Large Accuracy 0.561 # 16
Zero-Shot Cross-Lingual Transfer XTREME mT5 Sentence-pair Classification 89.8 # 4
Structured Prediction NA # 19
Question Answering 73.6 # 5
Sentence Retrieval NA # 18
Avg 40.9 # 18

Methods