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XTREME-S: Evaluating Cross-lingual Speech Representations ...
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mSLAM: Massively multilingual joint pre-training for speech and text ...
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Larger-Scale Transformers for Multilingual Masked Language Modeling ...
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Multilingual Speech Translation from Efficient Finetuning of Pretrained Models ...
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Unsupervised Cross-lingual Representation Learning for Speech Recognition ...
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Multilingual Speech Translation with Efficient Finetuning of Pretrained Models ...
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Unsupervised Cross-lingual Representation Learning at Scale ...
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Emerging Cross-lingual Structure in Pretrained Language Models ...
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Specializing distributional vectors of all words for lexical entailment
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What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
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In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018)
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XNLI: Evaluating Cross-lingual Sentence Representations ...
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Abstract:
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned ... : EMNLP 2018 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.1809.05053 https://arxiv.org/abs/1809.05053
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
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Very Deep Convolutional Networks for Text Classification
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In: European Chapter of the Association for Computational Linguistics EACL'17 ; https://hal.archives-ouvertes.fr/hal-01454940 ; European Chapter of the Association for Computational Linguistics EACL'17, 2017, Valencia, Spain (2017)
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What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties
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