Neural Text Normalization for Luxembourgish using Real-Life Variation Data

Anne-Marie Lutgen; Alistair Plum; Christoph Purschke; Barbara Plank

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arXiv · 2412.09383

Neural Text Normalization for Luxembourgish using Real-Life Variation Data

Anne-Marie Lutgen·Alistair Plum·Christoph Purschke·Barbara Plank

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Abstract

Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult task given the lack of annotated and parallel data, which is exacerbated by ongoing standardization. In this paper, we propose the first sequence-to-sequence normalization models using the ByT5 and mT5 architectures with training data obtained from word-level real-life variation data. We perform a fine-grained, linguistically-motivated evaluation to test byte-based, word-based and pipeline-based models for their strengths and weaknesses in text normalization. We show that our sequence model using real-life variation data is an effective approach for tailor-made normalization in Luxembourgish.

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Anne-Marie Lutgen, Alistair Plum, Christoph Purschke, Barbara Plank. 2024-12-12. Neural Text Normalization for Luxembourgish using Real-Life Variation Data. https://arxiv.org/abs/2412.09383

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