Summary of How Important Is Tokenization in French Medical Masked Language Models?, by Yanis Labrak et al.
How Important Is Tokenization in French Medical Masked Language Models?
by Yanis Labrak, Adrien Bazoge, Beatrice Daille, Mickael Rouvier, Richard Dufour
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper investigates the effectiveness of subword tokenization in natural language processing (NLP), particularly in the biomedical domain. Subword tokenization has become a widely used technique, driven by the success of pre-trained language models such as BPE, SentencePiece, and WordPiece. However, the factors contributing to its success remain unclear. The paper explores the optimal segmentation granularity for diverse tasks and languages, the influence of data sources on tokenizers, and the role of morphological information in Indo-European languages. Specifically, it analyzes classical tokenization algorithms like BPE and SentencePiece and introduces an original tokenization strategy that incorporates morpheme-enriched word segmentation into existing methods. This research aims to improve subword tokenization for biomedical terminology, which is characterized by specific rules governing morpheme combinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study looks at how we break down words into smaller parts in natural language processing (NLP). We’re trying to figure out what makes this way of breaking down words so successful. Right now, most NLP models use a technique called subword tokenization, which has become really popular thanks to the success of pre-trained language models. The study explores why this works and how we can improve it for special cases like biomedical terminology. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Tokenization
