Recently, it has been found that monolingual English language models can be
used as knowledge bases. Instead of structural knowledge base queries, masked
sentences such as "Paris is the capital of [MASK]" are used as probes. We
translate the established benchmarks TREx and GoogleRE into 53 languages.
Working with mBERT, we investigate three questions. (i) Can mBERT be used as a
multilingual knowledge base? Most prior work only considers English. Extending
research to multiple languages is important for diversity and accessibility.
(ii) Is mBERT's performance as knowledge base language-independent or does it
vary from language to language? (iii) A multilingual model is trained on more
text, e.g., mBERT is trained on 104 Wikipedias. Can mBERT leverage this for
better performance? We find that using mBERT as a knowledge base yields varying
performance across languages and pooling predictions across languages improves
performance. Conversely, mBERT exhibits a language bias; e.g., when queried in
Italian, it tends to predict Italy as the country of origin.

翻译 TREx 和 GoogleRE 两项基准测试为 53 种语言，使用 mBERT 模型研究其作为多语言知识库的性能及影响因素，发现其性能因被查询的语言而异，同时使用多种语言的预测结果可提高性能。

多语言 LAMA：探究多语言预训练语言模型中的知识

Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained  Language Models

We propose a simple and effective method for machine translation evaluation
which does not require reference translations. Our approach is based on (1)
grounding the entity mentions found in each source sentence and candidate
translation against a large-scale multilingual knowledge base, and (2)
measuring the recall of the grounded entities found in the candidate vs. those
found in the source. Our approach achieves the highest correlation with human
judgements on 9 out of the 18 language pairs from the WMT19 benchmark for
evaluation without references, which is the largest number of wins for a single
evaluation method on this task. On 4 language pairs, we also achieve higher
correlation with human judgements than BLEU. To foster further research, we
release a dataset containing 1.8 million grounded entity mentions across 18
language pairs from the WMT19 metrics track data.

提出了一种基于实体 grounding 以及实体回忆率来进行机器翻译评估的方法，无需参考翻译，并在多项测试中取得了高度的人类相关性表现，并通过数据集共享促进进一步研究。