评估大规模语言模型时代语言类型特征对多语言机器翻译的影响
Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models
AI总结：
本文研究了语言类型特征对多语言机器翻译质量的影响，通过分析两种先进模型发现目标语言类型显著影响翻译效果，并提出了细粒度的语言类型属性以促进进一步研究。
摘要：
尽管多语言建模取得了重大进展，但不同语言之间的质量差异仍然显著。除了明显的训练资源不均影响外，语言类型特性也被提出以确定语言建模的内在难度。然而，现有证据大多基于小规模单语语言模型或从头训练的双语翻译模型。我们扩展了这一研究方向，通过分析两种大型预训练多语言翻译模型，NLLB-200和Tower+，它们分别代表了编码器-解码器和解码器-only机器翻译的最新代表。基于广泛的语言种类，我们发现目标语言类型决定了两种模型的翻译质量，即使在控制了更多次要因素，如数据资源性和书写文字后也是如此。此外，具有某些语言类型特性的语言从更广泛的输出空间搜索中受益更多，表明这些语言可能从超越标准左到右束搜索的替代解码策略中获益。为了促进该领域的进一步研究，我们发布了针对FLORES+ MT评估基准中212种语言的细粒度语言类型属性。
英文摘要：
Despite major advances in multilingual modeling, large quality disparities persist across languages. Besides the obvious impact of uneven training resources, typological properties have also been proposed to determine the intrinsic difficulty of modeling a language. The existing evidence, however, is mostly based on small monolingual language models or bilingual translation models trained from scratch. We expand on this line of work by analyzing two large pre-trained multilingual translation models, NLLB-200 and Tower+, which are state-of-the-art representatives of encoder-decoder and decoder-only machine translation, respectively. Based on a broad set of languages, we find that target language typology drives translation quality of both models, even after controlling for more trivial factors, such as data resourcedness and writing script. Additionally, languages with certain typological properties benefit more from a wider search of the output space, suggesting that such languages could profit from alternative decoding strategies beyond the standard left-to-right beam search. To facilitate further research in this area, we release a set of fine-grained typological properties for 212 languages of the FLORES+ MT evaluation benchmark.
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