NormEval: A Unified Multi-Metric Framework for Evaluating Semantic Fidelity in Text Normalization
arXiv:2511.20409v2 Announce Type: replace Abstract: Text normalization methods such as stemming and lemmatization are fundamental components of NLP pipelines. As new normalization tools are developed for diverse languages, evaluation methodologies remain fragmented, relying on Compression Ratio, downstream accuracy, or sequence-to-sequence prediction scores in isolation, failing to distinguish between beneficial vocabulary reduction and harmful semantic distortion. Moreover, text normalization underpins intelligent systems in high-stakes domains, including clinical decision support and legal d
The proliferation of AI tools in diverse languages and sensitive domains necessitates more robust and unified evaluation frameworks for NLP foundational tasks like text normalization.
Improved text normalization evaluation directly impacts the reliability and semantic fidelity of AI systems, particularly in high-stakes applications where accuracy is critical.
The introduction of a unified multi-metric framework like NormEval standardizes the assessment of text normalization, moving beyond fragmented and isolated evaluation approaches.
- · NLP researchers
- · AI developers in high-stakes domains
- · Developers of multilingual AI models
- · AI systems with poor text normalization
- · Fragmented evaluation methodologies
More accurate and reliable text normalization components will be integrated into NLP pipelines.
This will lead to improved performance of AI systems in tasks relying on pre-processed text, especially in diverse linguistic contexts.
Enhanced semantic fidelity could indirectly reduce risks and increase adoption of AI in fields like clinical decision support and legal analysis.
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Read at arXiv cs.CL