Variation is the Norm: Embracing Sociolinguistics in NLP

2026-03-25

AI Summary

Natural language processing often treats linguistic variation as noise to be normalized, overlooking its sociolinguistic foundations and thereby compromising model robustness in real-world settings involving non-standard language forms. This work proposes the first systematic framework integrating sociolinguistic theory with NLP, advocating that linguistic variation—such as the orthographic diversity observed in Luxembourgish—should be modeled as a core feature rather than an artifact to be removed. The approach leverages sociolinguistically informed data construction, multivariate language modeling, and targeted fine-tuning to explicitly account for such variation. Experimental results demonstrate that ignoring linguistic variation significantly degrades model performance, whereas explicitly incorporating it enhances both generalization and robustness to non-standard linguistic forms.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

In Natural Language Processing (NLP), variation is typically seen as noise and "normalised away" before processing, even though it is an integral part of language. Conversely, studying language variation in social contexts is central to sociolinguistics. We present a framework to combine the sociolinguistic dimension of language with the technical dimension of NLP. We argue that by embracing sociolinguistics, variation can actively be included in a research setup, in turn informing the NLP side. To illustrate this, we provide a case study on Luxembourgish, an evolving language featuring a large amount of orthographic variation, demonstrating how NLP performance is impacted. The results show large discrepancies in the performance of models tested and fine-tuned on data with a large amount of orthographic variation in comparison to data closer to the (orthographic) standard. Furthermore, we provide a possible solution to improve the performance by including variation in the fine-tuning process. This case study highlights the importance of including variation in the research setup, as models are currently not robust to occurring variation. Our framework facilitates the inclusion of variation in the thought-process while also being grounded in the theoretical framework of sociolinguistics.

Problem

• language variation
• sociolinguistics
• NLP robustness
• orthographic variation
• natural language processing

Innovation

• sociolinguistics
• language variation
• orthographic variation
• NLP robustness
• inclusive NLP