论文标题
多语言蒙版语言模型的跨语性能力：语言结构的研究
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure
论文作者
论文摘要
多语言预训练的语言模型，例如Mbert和XLM-R，表现出令人印象深刻的跨语性能力。令人惊讶的是，他们俩都使用多语言蒙版语言模型（MLM），而没有任何跨语言监督或对齐数据。尽管取得了令人鼓舞的结果，但我们仍然缺乏清楚的了解，为什么跨语义能力从多语言传销中出现。在我们的工作中，我们认为跨语言能力来自语言之间的共同点。具体而言，我们研究了三种语言属性：组成顺序，组成和单词共发生。首先，我们通过用源语言修改属性来创建人造语言。然后，我们通过改变目标语言的跨语言转移结果来研究修改性属性的贡献。我们对六种语言和两项跨语性NLP任务进行实验（文本需要，句子检索）。我们的主要结论是，组成顺序和单词共发生的贡献是有限的，而该组成对于跨语言转移的成功更为至关重要。
Multilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability. Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned data. Despite the encouraging results, we still lack a clear understanding of why cross-lingual ability could emerge from multilingual MLM. In our work, we argue that cross-language ability comes from the commonality between languages. Specifically, we study three language properties: constituent order, composition and word co-occurrence. First, we create an artificial language by modifying property in source language. Then we study the contribution of modified property through the change of cross-language transfer results on target language. We conduct experiments on six languages and two cross-lingual NLP tasks (textual entailment, sentence retrieval). Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
