The Igbo language is facing a risk of becoming endangered, as indicated by a
2025 UNESCO study. This highlights the need to develop language technologies
for Igbo to foster communication, learning and preservation. To create robust,
impactful, and widely adopted language technologies for Igbo, it is essential
to incorporate the multi-dialectal nature of the language. The primary obstacle
in achieving dialectal-aware language technologies is the lack of comprehensive
dialectal datasets. In response, we present the IgboAPI dataset, a
multi-dialectal Igbo-English dictionary dataset, developed with the aim of
enhancing the representation of Igbo dialects. Furthermore, we illustrate the
practicality of the IgboAPI dataset through two distinct studies: one focusing
on Igbo semantic lexicon and the other on machine translation. In the semantic
lexicon project, we successfully establish an initial Igbo semantic lexicon for
the Igbo semantic tagger, while in the machine translation study, we
demonstrate that by finetuning existing machine translation systems using the
IgboAPI dataset, we significantly improve their ability to handle dialectal
variations in sentences.

为了发展 Igbo 语言的语言技术以促进沟通、学习和保护，本研究通过构建多方言的 Igbo-API 数据集，并应用于 Igbo 语义词典和机器翻译的研究中，有效地解决了 Igbo 语言技术面临的方言差异问题。

IgboAPI 数据集：通过多方言丰富增强 Igbo 语言技术

The IgboAPI Dataset: Empowering Igbo Language Technologies through  Multi-dialectal Enrichment

Identifying linguistic differences between dialects of a language often
requires expert knowledge and meticulous human analysis. This is largely due to
the complexity and nuance involved in studying various dialects. We present a
novel approach to extract distinguishing lexical features of dialects by
utilizing interpretable dialect classifiers, even in the absence of human
experts. We explore both post-hoc and intrinsic approaches to interpretability,
conduct experiments on Mandarin, Italian, and Low Saxon, and experimentally
demonstrate that our method successfully identifies key language-specific
lexical features that contribute to dialectal variations.

通过使用可解释的方言分类器，无论是否有人类专家，我们提供了一种提取方言区别词汇特征的新方法，并在普通话、意大利语和低萨克森语上进行实验，实验证明我们的方法成功地识别了导致方言变异的关键语言特定词汇特征。

通过可解释的方言分类器提取方言的词汇特征

Extracting Lexical Features from Dialects via Interpretable Dialect  Classifiers

Morphological tagging is challenging for morphologically rich languages due
to the large target space and the need for more training data to minimize model
sparsity. Dialectal variants of morphologically rich languages suffer more as
they tend to be more noisy and have less resources. In this paper we explore
the use of multitask learning and adversarial training to address morphological
richness and dialectal variations in the context of full morphological tagging.
We use multitask learning for joint morphological modeling for the features
within two dialects, and as a knowledge-transfer scheme for cross-dialectal
modeling. We use adversarial training to learn dialect invariant features that
can help the knowledge-transfer scheme from the high to low-resource variants.
We work with two dialectal variants: Modern Standard Arabic (high-resource
"dialect") and Egyptian Arabic (low-resource dialect) as a case study. Our
models achieve state-of-the-art results for both. Furthermore, adversarial
training provides more significant improvement when using smaller training
datasets in particular.

本文探索了利用多任务学习和敌对训练方法解决形态丰富的阿拉伯语言以及方言差异问题，并以现代标准阿拉伯语（高资源方言）和埃及阿拉伯语（低资源方言）为案例，在全面的形态标注中取得了最先进的结果。