Existing large language models (LLMs) that mainly focus on Standard American
English (SAE) often lead to significantly worse performance when being applied
to other English dialects. While existing mitigations tackle discrepancies for
individual target dialects, they assume access to high-accuracy dialect
identification systems. The boundaries between dialects are inherently
flexible, making it difficult to categorize language into discrete predefined
categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic
Aggregation), a modular approach to imbue SAE-trained models with
multi-dialectal robustness by composing adapters which handle specific
linguistic features. The compositional architecture of DADA allows for both
targeted adaptation to specific dialect variants and simultaneous adaptation to
various dialects. We show that DADA is effective for both single task and
instruction finetuned language models, offering an extensible and interpretable
framework for adapting existing LLMs to different English dialects.

提出了一种叫做 DADA 的模块化方法，通过将处理特定语言特征的适配器组合在一起，赋予了 SAE 训练模型多方言鲁棒性。 DADA 的组合架构允许针对特定的方言变体进行定向适应，同时适应多种方言。 通过实验证明，DADA 适用于单一任务和指令微调语言模型，为将现有的 LLMs 适应不同英语方言提供了一个可扩展和可解释的框架。

DADA: 动态聚合语言规则实现方言适应性

DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

With the advent of globalization, there is an increasing demand for
multilingual automatic speech recognition (ASR), handling language and
dialectal variation of spoken content. Recent studies show its efficacy over
monolingual systems. In this study, we design a large multilingual end-to-end
ASR using self-attention based conformer architecture. We trained the system
using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the
system performance handling: (i) monolingual (Ar, En and Fr); (ii)
multi-dialectal (Modern Standard Arabic, along with dialectal variation such as
Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and
dialectal (MSA-Egyptian dialect) test cases, and compare with current
state-of-the-art systems. Furthermore, we investigate the influence of
different embedding/character representations including character vs
word-piece; shared vs distinct input symbol per language. Our findings
demonstrate the strength of such a model by outperforming state-of-the-art
monolingual dialectal Arabic and code-switching Arabic ASR.

本研究采用自注意力机制，设计了一个大型的多语言端到端语音识别系统，使用阿拉伯语、英语和法语训练。我们评估了该系统在处理单语言、多方言以及代码转换等方面的表现，并与当前最先进的系统进行了比较。结果表明，我们的模型强于最先进的单语言方言阿拉伯语和代码转换阿拉伯语语音识别系统。