Recent studies have shown that multilingual pretrained language models can be
effectively improved with cross-lingual alignment information from Wikipedia
entities. However, existing methods only exploit entity information in
pretraining and do not explicitly use entities in downstream tasks. In this
study, we explore the effectiveness of leveraging entity representations for
downstream cross-lingual tasks. We train a multilingual language model with 24
languages with entity representations and show the model consistently
outperforms word-based pretrained models in various cross-lingual transfer
tasks. We also analyze the model and the key insight is that incorporating
entity representations into the input allows us to extract more
language-agnostic features. We also evaluate the model with a multilingual
cloze prompt task with the mLAMA dataset. We show that entity-based prompt
elicits correct factual knowledge more likely than using only word
representations. Our source code and pretrained models are available at
this https URL

此篇论文研究了如何利用来自 Wikipedia 实体的跨语言对齐信息来提高多语言预训练语言模型的效果，并探索了在下游跨语言任务中使用实体表示方法的有效性。