Despite much progress in recent years, the vast majority of work in natural
language processing (NLP) is on standard languages with many speakers. In this
work, we instead focus on low-resource languages and in particular
non-standardized low-resource languages. Even within branches of major language
families, often considered well-researched, little is known about the extent
and type of available resources and what the major NLP challenges are for these
language varieties. The first step to address this situation is a systematic
survey of available corpora (most importantly, annotated corpora, which are
particularly valuable for NLP research). Focusing on Germanic low-resource
language varieties, we provide such a survey in this paper. Except for
geolocation (origin of speaker or document), we find that manually annotated
linguistic resources are sparse and, if they exist, mostly cover morphosyntax.
Despite this lack of resources, we observe that interest in this area is
increasing: there is active development and a growing research community. To
facilitate research, we make our overview of over 80 corpora publicly
available. We share a companion website of this overview at
this https URL .

这篇论文对德国 ic 低资源语言变体的可用语料库进行了系统性调查，发现手工注释的语言资源稀缺，大多数仅涵盖形态和句法，但活跃的研究社区正在不断发展。作者还特意分享了超过 80 个语料库的大概率浏览与查看。

日耳曼语系低资源语言和方言语料库调查

A Survey of Corpora for Germanic Low-Resource Languages and Dialects

This paper proposes a framework to improve the typing experience of mobile
users in morphologically rich languages. Smartphone keyboards typically support
features such as input decoding, corrections and predictions that all rely on
language models. For latency reasons, these operations happen on device, so the
models are of limited size and cannot easily cover all the words needed by
users for their daily tasks, especially in morphologically rich languages. In
particular, the compounding nature of Germanic languages makes their vocabulary
virtually infinite. Similarly, heavily inflecting and agglutinative languages
(e.g. Slavic, Turkic or Finno-Ugric languages) tend to have much larger
vocabularies than morphologically simpler languages, such as English or
Mandarin. We propose to model such languages with automatically selected
subword units annotated with what we call binding types, allowing the decoder
to know when to bind subword units into words. We show that this method brings
around 20% word error rate reduction in a variety of compounding languages.
This is more than twice the improvement we previously obtained with a more
basic approach, also described in the paper.

本文提出了一个框架来提高手机用户在形态丰富的语言中的打字体验，该方法使用自动选择的子词单元来对德语等复杂的语言进行建模，并通过绑定类型注释来确定何时将子词单元绑定成单词，此方法在多种复杂语言中表现出约 20% 的错误率降低，是基本方法改进的两倍以上。