Summary of Myte: Morphology-driven Byte Encoding For Better and Fairer Multilingual Language Modeling, by Tomasz Limisiewicz and Terra Blevins and Hila Gonen and Orevaoghene Ahia and Luke Zettlemoyer
MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling
by Tomasz Limisiewicz, Terra Blevins, Hila Gonen, Orevaoghene Ahia, Luke Zettlemoyer
First submitted to arxiv on: 15 Mar 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 This paper addresses the issue of biased language modeling in multilingual settings by introducing a novel encoding paradigm called MYTE (Morpheme-based Yardstick for Text Encoding). Current methods exhibit a bias towards high-resource languages, leading to poor representation of underrepresented languages. MYTE encodes text using morphemes, which have more balanced inventories across languages compared to characters. The proposed method produces shorter encodings for all 99 analyzed languages, with significant improvements for non-European languages and non-Latin scripts. This advancement in multilingual language modeling leads to better performance and a reduced perplexity gap across diverse languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix a problem in how computers understand many different languages. Right now, some languages are harder for computers to understand because they’re not as well-studied or used by many people. The researchers came up with a new way to represent languages that’s more fair and accurate. They called it MYTE. This new method helps computers better understand many different languages, especially those spoken by fewer people. |
Keywords
* Artificial intelligence * Perplexity
