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

How can we identify the training examples that contribute most to the
prediction of a tree ensemble? In this paper, we introduce TREX, an explanation
system that provides instance-attribution explanations for tree ensembles, such
as random forests and gradient boosted trees. TREX builds on the representer
point framework previously developed for explaining deep neural networks. Since
tree ensembles are non-differentiable, we define a kernel that captures the
structure of the specific tree ensemble. By using this kernel in kernel
logistic regression or a support vector machine, TREX builds a surrogate model
that approximates the original tree ensemble. The weights in the kernel
expansion of the surrogate model are used to define the global or local
importance of each training example.
Our experiments show that TREX's surrogate model accurately approximates the
tree ensemble; its global importance weights are more effective in dataset
debugging than the previous state-of-the-art; its explanations identify the
most influential samples better than alternative methods under the remove and
retrain evaluation framework; it runs orders of magnitude faster than
alternative methods; and its local explanations can identify and explain errors
due to domain mismatch.

本文介绍了一种基于 REPresenter Point 框架的解释系统 TREX，它通过构建与特定树集成结构相对应的核来定义全局或局部的重要性，该系统的代理模型准确地近似了树集成，其重要性权重在数据集调试方面比以前的最新技术更加有效。