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Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ...
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Abstract:
Achieving universal translation between all human language pairs is the holy-grail of machine translation (MT) research. While recent progress in massively multilingual MT is one step closer to reaching this goal, it is becoming evident that extending a multilingual MT system simply by training on more parallel data is unscalable, since the availability of labeled data for low-resource and non-English-centric language pairs is forbiddingly limited. To this end, we present a pragmatic approach towards building a multilingual MT model that covers hundreds of languages, using a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting, even surpassing supervised translation quality for low- and mid-resource languages. We conduct a wide array of experiments to understand the effect of the degree of ...
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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.2201.03110 https://arxiv.org/abs/2201.03110
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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
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In: https://hal.inria.fr/hal-03177623 ; 2021 (2021)
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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets ...
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Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus ...
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