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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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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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Abstract:
Although proper handling of discourse phenomena significantly contributes to the quality of machine translation (MT), common translation quality metrics do not adequately capture them. Recent works in context-aware MT attempt to target a small set of these phenomena during evaluation. In this paper, we propose a new metric, P-CXMI, which allows us to identify translations that require context systematically and confirm the difficulty of previously studied phenomena as well as uncover new ones that have not been addressed in previous work. We then develop the Multilingual Discourse-Aware (MuDA) benchmark, a series of taggers for these phenomena in 14 different language pairs, which we use to evaluate context-aware MT. We find that state-of-the-art context-aware MT models find marginal improvements over context-agnostic models on our benchmark, which suggests current models do not handle these ambiguities effectively. We release code and data to invite the MT research community to increase efforts on ...
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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://dx.doi.org/10.48550/arxiv.2109.07446 https://arxiv.org/abs/2109.07446
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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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AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Measuring and Increasing Context Usage in Context-Aware Machine Translation ...
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On The Ingredients of an Effective Zero-shot Semantic Parser ...
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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
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Explorations in Transfer Learning for OCR Post-Correction ...
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Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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