| Challenge: | a new method for normalizing dialect transcripts is proposed for normative Finnish . dialectal Finnish is the common way of communication for people online in finnish . |
| Approach: | They propose a method for normalizing dialectal Finnish into the normative standard Finnish. |
| Outcome: | The proposed method lowers the initial word error rate of the corpus from 52.89 to 5.73 . it can be used as one processing step with many types of spoken language materials. |
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| Challenge: | Text normalization is a range of tasks that consist in replacing non-standard spellings with their standard equivalents. |
| Approach: | They introduce dialect-to-standard normalization as a sentence-level character transduction task and provide a large-scale analysis of these methods. |
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A Taxonomy for In-depth Evaluation of Normalization for User Generated Content (L18-1)
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| Challenge: | Existing taxonomies for lexical normalization are not suitable for the task of normalization since the categories are substantially different. |
| Approach: | They propose a taxonomy of error categories for lexical normalization . they annotate a recent normalization dataset and read a near-perfect agreement . |
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Dialect Normalization using Large Language Models and Morphological Rules (2025.findings-acl)
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| Challenge: | Natural language understanding systems struggle with low-resource languages, including many dialects of high-resourced ones. |
| Approach: | They propose a method that combines rule-based linguistically informed transformations and large language models with targeted few-shot prompting without any parallel data. |
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A Large-Scale Comparison of Historical Text Normalization Systems (N19-1)
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| Challenge: | a large study of historical text normalization is done on eight languages . there is no consensus on the state-of-the-art approach to normalization . |
| Approach: | They present a large study of historical text normalization done on eight languages . they evaluate four different systems based on supervised learning on datasets from eight different languages based in the literature . |
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Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches (P19-2)
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| Challenge: | a study using non-canonical text normalization shows that it can surpass the current best performing system by a large margin. |
| Approach: | They propose a fully automated, context-aware machine translation approach with fewer stages of processing. |
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Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing (2020.lrec-1)
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| Challenge: | Existing tools for lexical normalization of social media data are designed with canonical texts in mind, and this makes it difficult to process data in multiple languages. |
| Approach: | They propose to create a lexical normalization dataset for Italian and analyze the inter-annotator agreement for this task. |
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Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data? (2020.lrec-1)
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| Challenge: | Social media data is a valuable data resource for natural language processing tasks. |
| Approach: | They propose to adapt input text to a more standard form, a task also referred to as normalization. |
| Outcome: | The proposed system scores 94.29 accuracy on the test data compared to 95.22 when trained on human-annotated data. |
MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool (P19-3)
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| Challenge: | In this paper, we demonstrate the online demo and command line interface of a lexical normalization system (MoNoise) for a variety of languages. |
| Approach: | They propose to bundle seven datasets in six languages to form a new benchmark and a novel evaluation metric which is particularly suitable for cross-dataset comparisons. |
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Evaluating Historical Text Normalization Systems: How Well Do They Generalize? (N18-2)
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| Challenge: | Historical text normalization systems aim to convert historical wordforms to their modern equivalents . many of these systems have been developed and tested on a single language . |
| Approach: | They propose to use a nave baseline system to evaluate historical text normalization systems . they show that the models generalize well to unseen words in tests on five languages . |
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Standardisation of Dialect Comments in Social Networks in View of Sentiment Analysis : Case of Tunisian Dialect (2022.lrec-1)
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| Challenge: | Using the internet, the spoken Arabic dialect language becomes informal languages written in social media . this linguistic situation inhibits mutual understanding and makes computational approaches difficult . we present a pipeline to standardize the written texts in social networks by translating them to MSA . |
| Approach: | They propose a pipeline to standardize Arabic written texts by translating them to MSA . they use a bert-based model to select Tunisian Dialect from MSA and other dialects . |
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