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XTREME-S: Evaluating Cross-lingual Speech Representations ...
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One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia ...
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Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ...
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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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Charformer: Fast Character Transformers via Gradient-based Subword Tokenization ...
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XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ...
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Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ...
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XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ...
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A Call for More Rigor in Unsupervised Cross-lingual Learning ...
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Rethinking embedding coupling in pre-trained language models ...
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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Morphologically Aware Word-Level Translation
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In: Proceedings of the 28th International Conference on Computational Linguistics (2020)
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XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ...
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Abstract:
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 release the benchmark to encourage research on ... : In Proceedings of the 37th International Conference on Machine Learning (ICML). July 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2003.11080 https://dx.doi.org/10.48550/arxiv.2003.11080
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