We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models are freely available.
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Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference. While there is a growing number of sophisticated methods that aim to eliminate interference, our understanding of interference as a phenomenon is still limited. This work identifies the main factors that contribute to interference in multilingual machine translation. Through systematic experimentation, we find that interference (or synergy) are primarily determined by model size, data size, and the proportion of each language pair within the total dataset. We observe that substantial interference occurs mainly when the model is very small with respect to the available training data, and that using standard transformer configurations with less than one billion parameters largely alleviates interference and promotes synergy. Moreover, we show that tuning the sampling temperature to control the proportion of each language pair in the data is key to balancing the amount of interference between low and high resource language pairs effectively, and can lead to superior performance overall.
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在全球范围内消除语言障碍的目标的驱动下，机器翻译已巩固自己是当今人工智能研究的关键重点。但是，这样的努力围绕着一小部分语言结合在一起，留下了绝大多数低资源的语言。在确保安全，高质量的结果的同时，在牢记道德考虑的同时，打破200个语言障碍需要什么？没有留下的语言，我们首先通过与母语人士的探索性访谈来解决对低资源语言翻译支持的必要性来应对这一挑战。然后，我们创建了旨在缩小低资源和高资源语言之间的性能差距的数据集和模型。更具体地说，我们开发了一种有条件的计算模型，基于专家的稀疏混合物，该模型经过针对针对低资源语言量身定制的新颖有效的数据挖掘技术培训的。我们提出了多次建筑和培训改进，以抵消数千个任务的培训。至关重要的是，我们使用人类翻译的基准，Flores-200评估了40,000多种不同的翻译方向的性能，并将人类评估与新型毒性基准相结合，涵盖Flores-200的所有语言，以评估翻译安全性。我们的模型相对于先前的最新技术，实现了44％BLEU的改善，为实现通用翻译系统奠定了重要的基础。最后，我们开源此工作中描述的所有贡献，可在https://github.com/facebookresearch/fairseq/tree/nllb上访问。
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最先进的愿景和愿景和语言模型依靠大规模的Visio-linguisting预借鉴，以获得各种下游任务的良好性能。通常，这种模型通常是跨模态（对比）或多模态（具有早期融合）但不是两者;它们通常只针对特定的方式或任务。有希望的方向将是使用单一整体普遍模型，作为“基础”，目标是一次性的所有方式 - 真正的视觉和语言基础模型应该擅长视力任务，语言任务和交叉和多数模态视觉和语言任务。我们将Flava介绍在这样的模型中，并在跨越这些目标模式的广泛的35个任务上展示令人印象深刻的性能。
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