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AUTOLEX: An Automatic Framework for Linguistic Exploration ...
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MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages ...
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A Systematic Evaluation of Large Language Models of Code ...
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Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ...
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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
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In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022)
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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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Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ...
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Few-shot Language Coordination by Modeling Theory of Mind ...
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Systematic Inequalities in Language Technology Performance across the World's Languages ...
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Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
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MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ...
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Abstract:
The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an under-studied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis ... : 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) ...
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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/2104.07908 https://dx.doi.org/10.48550/arxiv.2104.07908
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XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ...
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When Does Translation Require Context? A Data-driven, Multilingual Exploration ...
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Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ...
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Distributionally Robust Multilingual Machine Translation ...
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