A majority of language technologies are tailored for a small number of
high-resource languages, while relatively many low-resource languages are
neglected. One such group, Creole languages, have long been marginalized in
academic study, though their speakers could benefit from machine translation
(MT). These languages are predominantly used in much of Latin America, Africa
and the Caribbean. We present the largest cumulative dataset to date for Creole
language MT, including 14.5M unique Creole sentences with parallel translations
-- 11.6M of which we release publicly, and the largest bitexts gathered to date
for 41 languages -- the first ever for 21. In addition, we provide MT models
supporting all 41 Creole languages in 172 translation directions. Given our
diverse dataset, we produce a model for Creole language MT exposed to more
genre diversity than ever before, which outperforms a genre-specific Creole MT
model on its own benchmark for 23 of 34 translation directions.

对于学术研究长期以来忽视的克里奥尔语言，我们提供了到目前为止最大的克里奥尔语机器翻译累积数据集，包括 14.5M 句独特的克里奥尔语句子和平行翻译，其中公开发布的有 11.6M 个；此外，我们还提供了支持 172 个翻译方向的 41 种克里奥尔语言的机器翻译模型，利用这个多样化的数据集，我们的模型在 23 个翻译方向的 34 个中超过了一种专门用于克里奥尔语的模型。

Kreyòl-MT: 构建拉美、加勒比和殖民地非洲克里奥尔语的机器翻译系统

Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial  African Creole Languages

While major languages often enjoy substantial attention and resources, the
linguistic diversity across the globe encompasses a multitude of smaller,
indigenous, and regional languages that lack the same level of computational
support. One such region is the Caribbean. While commonly labeled as "English
speaking", the ex-British Caribbean region consists of a myriad of Creole
languages thriving alongside English. In this paper, we present Guylingo: a
comprehensive corpus designed for advancing NLP research in the domain of
Creolese (Guyanese English-lexicon Creole), the most widely spoken language in
the culturally rich nation of Guyana. We first outline our framework for
gathering and digitizing this diverse corpus, inclusive of colloquial
expressions, idioms, and regional variations in a low-resource language. We
then demonstrate the challenges of training and evaluating NLP models for
machine translation in Creole. Lastly, we discuss the unique opportunities
presented by recent NLP advancements for accelerating the formal adoption of
Creole languages as official languages in the Caribbean.

通过 Guylingo 语料库，本文研究了加勒比海地区的克里奥尔语（Guyanese English-lexicon Creole），强调了在资源匮乏的语言环境下，自然语言处理对于克里奥尔语的挑战和发展机遇。

Guylingo: 圭亞那克里奧爾語語料庫

Guylingo: The Republic of Guyana Creole Corpora

We aim to learn language models for Creole languages for which large volumes
of data are not readily available, and therefore explore the potential transfer
from ancestor languages (the 'Ancestry Transfer Hypothesis'). We find that
standard transfer methods do not facilitate ancestry transfer. Surprisingly,
different from other non-Creole languages, a very distinct two-phase pattern
emerges for Creoles: As our training losses plateau, and language models begin
to overfit on their source languages, perplexity on the Creoles drop. We
explore if this compression phase can lead to practically useful language
models (the 'Ancestry Bottleneck Hypothesis'), but also falsify this. Moreover,
we show that Creoles even exhibit this two-phase pattern even when training on
random, unrelated languages. Thus Creoles seem to be typological outliers and
we speculate whether there is a link between the two observations.

本文探讨使用祖先语言传递学习的方法来学习数据不充足的克里奥尔语的语言模型，但发现标准的传递方法并不适用。与其他非克里奥尔语不同的是，Creoles 表现出了一种明显的两阶段模式，我们探索其中的 Ancestry Bottleneck Hypothesis，但也证明了否定该假设，此外，我们证明了即使训练随机无关语言的情况下 Creoles 仍表现出这种二阶段模式，这使得 Creoles 成为了类型学上的离群值，我们猜测这两个观察结果之间是否有联系。

祖先语到克里奥尔语的转换并非易事

Ancestor-to-Creole Transfer is Not a Walk in the Park

In recent years, the natural language processing (NLP) community has given
increased attention to the disparity of efforts directed towards high-resource
languages over low-resource ones. Efforts to remedy this delta often begin with
translations of existing English datasets into other languages. However, this
approach ignores that different language communities have different needs. We
consider a group of low-resource languages, Creole languages. Creoles are both
largely absent from the NLP literature, and also often ignored by society at
large due to stigma, despite these languages having sizable and vibrant
communities. We demonstrate, through conversations with Creole experts and
surveys of Creole-speaking communities, how the things needed from language
technology can change dramatically from one language to another, even when the
languages are considered to be very similar to each other, as with Creoles. We
discuss the prominent themes arising from these conversations, and ultimately
demonstrate that useful language technology cannot be built without involving
the relevant community.

本文探讨了低资源语言社区中克里奥尔语言的需要，发现不同的语言社区对语言技术的需求存在巨大差异，并强调没有社区参与是无法构建有用的语言技术的。