Dialect Normalization using Large Language Models and Morphological Rules (2025)

Abstract: Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle this issue by transforming dialectal text so that it can be used by standard-language tools downstream. In this study, we tackle this task by introducing a new normalization method that combines rule-based linguistically informed transformations and large language models (LLMs) with targeted few-shot prompting, without requiring any parallel data. We implement our method for Greek dialects and apply it on a dataset of regional proverbs, evaluating the outputs using human annotators. We then use this dataset to conduct downstream experiments, finding that previous results regarding these proverbs relied solely on superficial linguistic information, including orthographic artifacts, while new observations can still be made through the remaining semantics.

## introduction natural language processing has long struggled with lower-resource language varieties, including geographic varieties of higher-resource standardized ones @xcite . according to members of such language communities, who are usually also speakers of the equivalent standard, natural language understanding (nlu) of dialectal text is much more important than language generation (nlg) into the local variety @xcite . this variation in demand highlights the significance of transforming dialectal text into a standard variety while maintaining as much of the original meaning as possible, which is known as dialect-tostandard normalization. that is because by improving our methods in this area we will be able to apply modern nlu techniques to a vast array of formerly 1 we publicly release all code and datasets produced for this work: https://github.com/andhmak/rule _ dialnorm source dialectal sentence src: ου θεός κι ου γείτονας. baseline normalization →: ούτε ο θεός, ούτε ο γείτονας. x /úte o theós, úte o jítonas/ neither god nor the neighbour. proposed rule-enhanced @xmath0. in this work, we introduce a novel method for normalizing dialectal data into a standard variety. our proposed method first applies morphological rules, specified based on dialect-specific linguistic prior knowledge, and then feeds the preprocessed input to an llm along with dialect-specific shots. this second step enhances the input with sentences exhibiting those facultative dialectal features which are not addressable only with the first step. we implement the language-specific parts of this procedure for a set of greek dialects represented in a large dataset of regional proverbs. an example of our method compared to simple prompting for one of the proverbs in our dataset is shown in figure 1 . we then experiment with two different llms and ablate the rule-based step, using human annotators. we thus produce a new normalized dataset in standard modern greek, which we use in downstream tasks: first, we replicate prior research using the newly-standardized proverbs to ascertain whether the previous results depended on the semantics or on the now-removed linguistic peculiarities of each variety and its transcription method. additionally, we conduct further experiments showcasing the usability of our dataset for obtaining semantic, non-dialectally-colored insights into a set of originally dialectal texts. in short, we make the following contributions: • we propose a new method for normalizing dialectal speech, using a pipeline of rule-based transformations followed by an llm with a few dialect-specific examples. previous work has been carried out in the area of dialect normalization, targeting specific varieties @xcite @xcite , as well as more generalized approaches @xcite . recently, pretrained multilingual llms have proven useful in such tasks, especially when finetuned on parallel dialectal-standard data (ibn alam and anastasopoulos, 2025). these kinds of parallel datasets are in some way or another required in all these past techniques in order to train specialized models. in contrast, our technique eliminates this requirement by leveraging llms' tendency to treat unseen dialectal features as noise, combined with the exploitation of linguistic knowledge of the dialects in question and as few as three parallel sentences for few-shot prompting. this makes our approach viable even for use cases such as the one we explore where there are practically no parallel text data available. pavlopoulos et al. (2024) introduced a machineactionable dataset of greek proverbs, comprising over 100,000 proverb variants, each originating from one of 134 unique locations across greece. an exploration of the spatial distribution of proverbs showed that the most widespread proverbs come from the mainland while the least come primarily from the islands. using the latter, then, they showed that text geolocation/geocoding @xcite @xcite ) can be accurate for specific locations, and that conventional machine learning algorithms operating on stylistic features outperformed transfer learning. we argue, however, that relying on the superficial linguistic features of the original (non-normalized) text, instead of semantic ones, makes it hard to determine shared semantics or any (possibly deeper) cultural connections across different regions. ## methodology our normalization method consists of two steps. first, we preprocess our inputs using a rule-based procedure. then, we pass the previous step's output to an llm with few-shot prompting. part 1: rule-based normalization (rbn) rbn is achieved by string replacements of specific character sequences according to the linguistic features of each dialect compared to the standard. we divide the greek dialects into three groups, following established literature @xcite : northern, southern and pontic, according to their features, and use different transformation rules for each group. the dialects' specific distribution among these groups is described in appendix a, and indicative examples of string replacements are in appendix b. the amount of linguistic knowledge required is roughly what would be present in a dialect's comparative grammar, in our case amounting to 14 string replacement rules. part 2: few-shot prompting our prompts are designed to guide the model to perform our desired task while also providing the llm with the necessary linguistic information, which is otherwise difficult to encode using rules. first, we include the name of the region our text is sourced from (especially helpful if the model has seen relevant data during pre-training). second, we provide instructions to only change the dialect, so that it conforms with the standard, without affecting the style of the text. otherwise, we notice that llms tend to view dialectal features as signs of informality, and therefore produce overly formal text when not explicitly directed not to. similarly, they seem to replace vocabulary existing in both the dialect and the standard with alternatives. hence, we also instruct for lexical terms to only be replaced when they are absent from the standard. finally, we provide a few examples of the task being performed successfully, specifically selected to display dialectal features not encoded in the previous step. the full prompt used per dialectal group is provided in appendix c. ## normalization experiments dataset we perform our experiments on the dataset provided by @xcite , specifically on the balanced corpus, containing 500 proverbs from each geographic location, which was also used for their experiments. models for the llm-based part of our normalization method, we use gpt-4o (gpt-4o-2024-11-20|; @xcite as well as the llama 3.1-70b @xcite . overall we explore four different setups: 1. gpt 3s+rbn uses gpt-4o and follows the entire pipeline as described in section 3; 2. gpt 3s only uses the 3-shot prompting method, using a different prompt according to the group of the input dialect, skipping rbn; 3. llama 3s+rbn uses llama 3 and also follows the entire pipeline; and 4. llama 9s uses llama 3 and skips both rbn and the division into dialectal groups, providing all three parallel examples of all three dialectal groups in every prompt.