多语言BERT带有口音：评估多语言模型中英语对流利度的影响
Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models
AI总结：
本文发现多语言BERT存在语法结构偏差，即英语等高资源语言的语法会渗入低资源语言，并通过西班牙语代词省略和希腊语主谓顺序实验证明其偏向英语式结构，呼吁更关注语言学意识的流利度评估。
摘要：
虽然多语言语言模型可以通过利用资源较丰富的语言来提升低资源语言上的自然语言处理性能，但它们也会降低所有语言上的平均性能（即“多语言诅咒”）。在此我们展示多语言模型的另一个问题：资源较丰富语言中的语法结构会渗入资源较贫乏的语言，我们将这一现象称为语法结构偏差。我们通过一种比较新颖的方法来展示这种偏差，该方法将多语言模型的流利度与单语西班牙语和希腊语模型的流利度进行比较：测试它们对两种精心挑选的可变语法结构的偏好（西班牙语中的可选代词省略和希腊语中的可选主谓顺序）。我们发现，与我们的单语控制语言模型相比，多语言BERT偏向于类似英语的设置（显式代词和主谓宾顺序）。通过我们的案例研究，我们希望揭示多语言模型可能产生偏差的细粒度方式，并鼓励更具语言学意识的流利度评估。
英文摘要：
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 language model. With our case studies, we hope to bring to light the fine-grained ways in which multilingual models can be biased,and encourage more linguistically-aware fluency evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
