Knowledge bases (KBs) contain plenty of structured world and commonsense
knowledge. As such, they often complement distributional text-based information
and facilitate various downstream tasks. Since their manual construction is
resource- and time-intensive, recent efforts have tried leveraging large
pretrained language models (PLMs) to generate additional monolingual knowledge
facts for KBs. However, such methods have not been attempted for building and
enriching multilingual KBs. Besides wider application, such multilingual KBs
can provide richer combined knowledge than monolingual (e.g., English) KBs.
Knowledge expressed in different languages may be complementary and unequally
distributed: this implies that the knowledge available in high-resource
languages can be transferred to low-resource ones. To achieve this, it is
crucial to represent multilingual knowledge in a shared/unified space. To this
end, we propose a unified representation model, Prix-LM, for multilingual KB
construction and completion. We leverage two types of knowledge, monolingual
triples and cross-lingual links, extracted from existing multilingual KBs, and
tune a multilingual language encoder XLM-R via a causal language modeling
objective. Prix-LM integrates useful multilingual and KB-based factual
knowledge into a single model. Experiments on standard entity-related tasks,
such as link prediction in multiple languages, cross-lingual entity linking and
bilingual lexicon induction, demonstrate its effectiveness, with gains reported
over strong task-specialised baselines.

本研究通过提出 Prix-LM 模型，利用大型预训练语言模型在多种语言间进行联合表示，构建和完善多语言知识库，并在实体相关任务方面展示其有效性。

Prix-LM：多语言知识库构建预训练

Prix-LM: Pretraining for Multilingual Knowledge Base Construction

Text discourse parsing plays an important role in understanding information
flow and argumentative structure in natural language. Previous research under
the Rhetorical Structure Theory (RST) has mostly focused on inducing and
evaluating models from the English treebank. However, the parsing tasks for
other languages such as German, Dutch, and Portuguese are still challenging due
to the shortage of annotated data. In this work, we investigate two approaches
to establish a neural, cross-lingual discourse parser via: (1) utilizing
multilingual vector representations; and (2) adopting segment-level translation
of the source content. Experiment results show that both methods are effective
even with limited training data, and achieve state-of-the-art performance on
cross-lingual, document-level discourse parsing on all sub-tasks.

研究了通过利用多语言向量表示和采用源内容的分段级别翻译建立神经交叉语言篇章分析器的两种方法，并表明这两种方法即使在有限的训练数据下也有效，并且在所有子任务上实现了跨语言、文档级的篇章分析性能。