It is well known that AI-based language technology -- large language models,
machine translation systems, multilingual dictionaries, and corpora -- is
currently limited to 2 to 3 percent of the world's most widely spoken and/or
financially and politically best supported languages. In response, recent
research efforts have sought to extend the reach of AI technology to
``underserved languages.'' In this paper, we show that many of these attempts
produce flawed solutions that adhere to a hard-wired representational
preference for certain languages, which we call techno-linguistic bias.
Techno-linguistic bias is distinct from the well-established phenomenon of
linguistic bias as it does not concern the languages represented but rather the
design of the technologies. As we show through the paper, techno-linguistic
bias can result in systems that can only express concepts that are part of the
language and culture of dominant powers, unable to correctly represent concepts
from other communities. We argue that at the root of this problem lies a
systematic tendency of technology developer communities to apply a simplistic
understanding of diversity which does not do justice to the more profound
differences that languages, and ultimately the communities that speak them,
embody. Drawing on the concept of epistemic injustice, we point to the broader
sociopolitical consequences of the bias we identify and show how it can lead
not only to a disregard for valuable aspects of diversity but also to an
under-representation of the needs and diverse worldviews of marginalized
language communities.

通过本文的研究表明，AI 技术的推广仍存在技术语言偏见的问题，这导致一些系统无法正确地表达来自其他社区的概念，从而忽视多样性和边缘化语言社群的需求和世界观。

多样性与语言技术：如何科技语言偏见导致知识公正失衡

Diversity and Language Technology: How Techno-Linguistic Bias Can Cause  Epistemic Injustice

Recent progress in NLP is driven by pretrained models leveraging massive
datasets and has predominantly benefited the world's political and economic
superpowers. Technologically underserved languages are left behind because they
lack such resources. Hundreds of underserved languages, nevertheless, have
available data sources in the form of interlinear glossed text (IGT) from
language documentation efforts. IGT remains underutilized in NLP work, perhaps
because its annotations are only semi-structured and often language-specific.
With this paper, we make the case that IGT data can be leveraged successfully
provided that target language expertise is available. We specifically advocate
for collaboration with documentary linguists. Our paper provides a roadmap for
successful projects utilizing IGT data: (1) It is essential to define which NLP
tasks can be accomplished with the given IGT data and how these will benefit
the speech community. (2) Great care and target language expertise is required
when converting the data into structured formats commonly employed in NLP. (3)
Task-specific and user-specific evaluation can help to ascertain that the tools
which are created benefit the target language speech community. We illustrate
each step through a case study on developing a morphological reinflection
system for the Tsimchianic language Gitksan.

本研究指出，利用语言学家的专业知识，IGT 数据可以成功应用于 NLP 项目中，但需要定义任务、语言专业知识和任务评估等步骤，本文以 Gitksan 语 Morphological Reinflection 系统为例详细介绍。

低文档化语言 NLP 中语言专业知识的意义

Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages

While Wikipedia exists in 287 languages, its content is unevenly distributed
among them. In this work, we investigate the generation of open domain
Wikipedia summaries in underserved languages using structured data from
Wikidata. To this end, we propose a neural network architecture equipped with
copy actions that learns to generate single-sentence and comprehensible textual
summaries from Wikidata triples. We demonstrate the effectiveness of the
proposed approach by evaluating it against a set of baselines on two languages
of different natures: Arabic, a morphological rich language with a larger
vocabulary than English, and Esperanto, a constructed language known for its
easy acquisition.

本文探讨如何使用 Wikidata 中的结构化数据来生成在 Wikipedia 贫穷语言中的开放领域概要。 为此，作者提出了一种神经网络结构，该结构配备有复制操作，可以学习从 Wikidata 三元组中生成单个句子和可理解的文本摘要。作者在阿拉伯语和世界语这两种不同特性的语言上对所提出的方法进行了评估，并证明了它的有效性。