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The effect of domain and diacritics in Yorùbá-English neural machine translation
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In: 18th Biennial Machine Translation Summit ; https://hal.inria.fr/hal-03350967 ; 18th Biennial Machine Translation Summit, Aug 2021, Orlando, United States (2021)
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The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation ...
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Abstract:
Massively multilingual machine translation (MT) has shown impressive capabilities, including zero and few-shot translation between low-resource language pairs. However, these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper, we present MENYO-20k, the first multi-domain parallel corpus with a special focus on clean orthography for Yorùbá--English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality, we also analyze the effect of diacritics, a major characteristic of Yorùbá, in the training data. We investigate how and when this training condition ... : Accepted to MT Summit 2021 (Research Track) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2103.08647 https://arxiv.org/abs/2103.08647
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EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT ...
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Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces ...
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