Application domains such as digital humanities and tool like chatbots involve
some form of processing natural language, from digitising hardcopies to speech
generation. The language of the content is typically characterised as either a
low resource language (LRL) or high resource language (HRL), also known as
resource-scarce and well-resourced languages, respectively. African languages
have been characterized as resource-scarce languages (Bosch et al. 2007;
Pretorius & Bosch 2003; Keet & Khumalo 2014) and English is by far the most
well-resourced language. Varied language resources are used to develop software
systems for these languages to accomplish a wide range of tasks. In this paper
we argue that the dichotomous typology LRL and HRL for all languages is
problematic. Through a clear understanding of language resources situated in a
society, a matrix is developed that characterizes languages as Very LRL, LRL,
RL, HRL and Very HRL. The characterization is based on the typology of
contextual features for each category, rather than counting tools, and
motivation is provided for each feature and each characterization. The
contextualisation of resourcedness, with a focus on African languages in this
paper, and an increased understanding of where on the scale the language used
in a project is, may assist in, among others, better planning of research and
implementation projects. We thus argue in this paper that the characterization
of language resources within a given scale in a project is an indispensable
component particularly in the context of low-resourced languages.

通过对语言资源进行清晰的了解，并针对每个类别提供特征和特征的动机，本文开发了一个将语言划分为非常低资源语言、低资源语言、中资源语言、高资源语言和非常高资源语言的矩阵。通过对非洲语言进行语境化和增加对项目中所使用的语言所处范围的理解，可以提供助力于改善研究和实施项目规划等方面的帮助。因此，本文在低资源语言的背景下，论文认为在项目中将语言资源的特征划分为给定的规模是不可或缺的组成部分。

语言资源水平对文本数字处理的语境化

Contextualising Levels of Language Resourcedness affecting Digital  Processing of Text

As pre-trained language models become more resource-demanding, the inequality
between resource-rich languages such as English and resource-scarce languages
is worsening. This can be attributed to the fact that the amount of available
training data in each language follows the power-law distribution, and most of
the languages belong to the long tail of the distribution. Some research areas
attempt to mitigate this problem. For example, in cross-lingual transfer
learning and multilingual training, the goal is to benefit long-tail languages
via the knowledge acquired from resource-rich languages. Although being
successful, existing work has mainly focused on experimenting on as many
languages as possible. As a result, targeted in-depth analysis is mostly
absent. In this study, we focus on a single low-resource language and perform
extensive evaluation and probing experiments using cross-lingual post-training
(XPT). To make the transfer scenario challenging, we choose Korean as the
target language, as it is a language isolate and thus shares almost no typology
with English. Results show that XPT not only outperforms or performs on par
with monolingual models trained with orders of magnitudes more data but also is
highly efficient in the transfer process.

本研究采用跨语言后训练 (XPT) 基于单一低资源语言进行了广泛的评估和探测实验，结果显示 XPT 不仅优于或与训练数据数量更多的单语模型的表现相当，而且转移过程非常高效。