Summary of Training a Bilingual Language Model by Mapping Tokens Onto a Shared Character Space, By Aviad Rom and Kfir Bar
Training a Bilingual Language Model by Mapping Tokens onto a Shared Character Space
by Aviad Rom, Kfir Bar
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper introduces a bilingual language model for Arabic and Hebrew, which utilizes a unified script to represent both languages. By transliterating Arabic texts into Hebrew, the model can leverage the morphological and structural similarities between the two languages, as well as their shared cognates. The authors train the model on a dataset approximately 60% smaller than existing models and demonstrate its effectiveness in machine translation tasks. Specifically, they show that the model outperforms a control model that uses the original Arabic script for Arabic texts, highlighting the benefits of the transliteration step. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a bilingual language model for Arabic and Hebrew by using the same script for both languages. This helps the model understand the similarities between the two languages. The authors test the model’s ability to translate text from one language to another. They find that their model works well, even though it was trained on less data than other models. |
Keywords
* Artificial intelligence * Language model * Translation
