Learning better sentence embeddings leads to improved performance for natural
language understanding tasks including semantic textual similarity (STS) and
natural language inference (NLI). As prior studies leverage large-scale labeled
NLI datasets for fine-tuning masked language models to yield sentence
embeddings, task performance for languages other than English is often left
behind. In this study, we directly compared two data augmentation techniques as
potential solutions for monolingual STS: (a) cross-lingual transfer that
exploits English resources alone as training data to yield non-English sentence
embeddings as zero-shot inference, and (b) machine translation that coverts
English data into pseudo non-English training data in advance. In our
experiments on monolingual STS in Japanese and Korean, we find that the two
data techniques yield performance on par. Rather, we find a superiority of the
Wikipedia domain over the NLI domain for these languages, in contrast to prior
studies that focused on NLI as training data. Combining our findings, we
demonstrate that the cross-lingual transfer of Wikipedia data exhibits improved
performance, and that native Wikipedia data can further improve performance for
monolingual STS.

通过比较两种数据技术（跨语言转移和机器翻译），研究发现，使用英文数据训练的跨语言转移和使用英文数据转换为伪非英文训练数据的机器翻译在日语和韩语的单语 STS 上表现相当。此外，研究还发现 Wikipedia 领域在这些语言上优于 NLI 领域，并结合这些发现实验表明，跨语言转移 Wikipedia 数据能够提高单语 STS 的性能。

跨语言转移还是机器翻译？关于单语义文本相似性的数据增强

Cross-lingual Transfer or Machine Translation? On Data Augmentation for  Monolingual Semantic Textual Similarity

This article presents the application of the Universal Named Entity framework
to generate automatically annotated corpora. By using a workflow that extracts
Wikipedia data and meta-data and DBpedia information, we generated an English
dataset which is described and evaluated. Furthermore, we conducted a set of
experiments to improve the annotations in terms of precision, recall, and
F1-measure. The final dataset is available and the established workflow can be
applied to any language with existing Wikipedia and DBpedia. As part of future
research, we intend to continue improving the annotation process and extend it
to other languages.

本文介绍了使用 Universal Named Entity 框架来自动生成注释语料库的应用。通过使用从维基百科数据和元数据以及 DBpedia 信息中提取出来的工作流程，我们生成了一个英语数据集并进行了描述和评估。此外，我们进行了一系列实验，以在精确度，召回率和 F1-measure 方面改善注释。最终数据集可供使用，建立的工作流程可应用于任何具有现有维基百科和 DBpedia 的语言。作为未来研究的一部分，我们打算继续改进注释过程并将其扩展到其他语言。

构建并评估通用命名实体识别英语语料库

Building and Evaluating Universal Named-Entity Recognition English corpus

Recent work in neural generation has attracted significant interest in
controlling the form of text, such as style, persona, and politeness. However,
there has been less work on controlling neural text generation for content.
This paper introduces the notion of Content Transfer for long-form text
generation, where the task is to generate a next sentence in a document that
both fits its context and is grounded in a content-rich external textual source
such as a news story. Our experiments on Wikipedia data show significant
improvements against competitive baselines. As another contribution of this
paper, we release a benchmark dataset of 640k Wikipedia referenced sentences
paired with the source articles to encourage exploration of this new task.

该研究提出了内容转移技术，该技术可以在长文本生成中通过引用外部文本源，例如新闻报道，生成适合文本上下文并富含内容的下一句话。我们在 Wikipedia 数据上的实验结果显示出明显的改进，并在此次研究中发布了 640K Wikipedia 参考句子及源文章的基准数据集。