While multilingual language models can improve NLP performance on low-resource languages by leveraging higher-resource languages, they also reduce average performance on all languages (the 'curse of multilinguality'). Here we show another problem with multilingual models: grammatical structures in higher-resource languages bleed into lower-resource languages, a phenomenon we call grammatical structure bias. We show this bias via a novel method for comparing the fluency of multilingual models to the fluency of monolingual Spanish and Greek models: testing their preference for two carefully-chosen variable grammatical structures (optional pronoun-drop in Spanish and optional Subject-Verb ordering in Greek). We find that multilingual BERT is biased toward the English-like setting (explicit pronouns and Subject-Verb-Object ordering) as compared to our monolingual control. With our case studies, we hope to bring to light the fine-grained ways in which dominant languages can affect and bias multilingual performance, and encourage more linguistically-aware fluency evaluation.

研究发现多语种模型中高资源语言的语法结构会渗透到低资源语言中，给跨语言模型性能带来偏差。

多语言BERT有口音：评估英语对多语言模型流畅性的影响