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MYTE: Morphology-Driven Byte Encoding for Multilingual Language Modeling


Core Concepts
Introducing MYTE encoding for fairer and more efficient multilingual language modeling.
Abstract
The article introduces MYTE, a new encoding paradigm based on morphemes to address disparities in text representation across languages. MYTE produces shorter encodings for 99 languages, especially benefiting non-European languages and non-Latin scripts. The method improves multilingual LM performance and reduces perplexity gaps. Byte-level models aim to solve over-segmentation issues in byte sequences, particularly for non-Latin script languages. The proposed methodology rearranges byte codepages to free space used to encode morphemes, improving segmentation granularity across languages. Equitable text representation is achieved by ensuring similar encoded sequence lengths across parallel texts in different languages.
Stats
EN: roughly at 12 utf-8 72 6F 75 67 68 6C 79 at 31 32 myte 52 82 A3 93 6C 79 at 31 32 CS: přibližně ve...
Quotes
"We propose a novel byte-encoding method that is morphologically driven." "Our encoding convention (MYTE) is based on morphemes, as their inventories are more balanced across languages than characters." "Our contributions can be summarized as proposing a novel byte-encoding method that is morphologically driven."

Key Insights Distilled From

by Tomasz Limis... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10691.pdf
MYTE

Deeper Inquiries

How does the MYTE encoding compare to other tokenization methods in terms of efficiency and fairness

The MYTE encoding method stands out from other tokenization methods in terms of efficiency and fairness. In terms of efficiency, MYTE significantly reduces the length of encoded sequences for all languages analyzed, leading to a notable decrease in computation costs and benefiting non-Latin script languages the most. This improved compression rate is crucial for enhancing the performance and inference speed of language models, especially when operating on longer sequences that are common in non-Latin scripts. Additionally, MYTE ensures equitable segmentation granularity across diverse languages by assigning byte codes of similar lengths to morphemes. This balanced representation leads to more efficient language modeling performance and diminishes disparities in perplexity levels across different languages.

What potential challenges or limitations might arise when extending the MYTE encoding to additional languages

Extending the MYTE encoding to additional languages may present some challenges or limitations. One potential challenge is ensuring accurate morphological analysis for new languages without access to high-quality annotated data or lexicons like Wikipedia articles used in training Morfessor. The effectiveness of MYTE relies on precise segmentation based on morphemes, so inadequate data resources could lead to errors or over-segmentation during encoding. Another limitation could be related to unseen scripts where MYTE might not provide significant improvements due to unfamiliar character sets or unique linguistic features that require specific handling beyond standard morphology-driven approaches.

How could the concept of morphology-driven encoding impact the development of future language models beyond multilingual applications

The concept of morphology-driven encoding introduced by MYTE has broader implications for future language model development beyond multilingual applications. By focusing on meaningful constituents like morphemes rather than individual characters or bytes, this approach can enhance the interpretability and generalizability of language models across various tasks and domains. Morphology-driven encoding can improve model performance by capturing linguistic structures more effectively, leading to better understanding and generation capabilities in natural language processing tasks such as machine translation, text generation, sentiment analysis, etc. Furthermore, this approach could inspire innovations in tokenization strategies that prioritize semantic units over orthographic symbols, paving the way for more efficient and accurate language processing systems with enhanced cross-lingual capabilities.
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