Summary of One Model Is All You Need: Byt5-sanskrit, a Unified Model For Sanskrit Nlp Tasks, by Sebastian Nehrdich et al.
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks
by Sebastian Nehrdich, Oliver Hellwig, Kurt Keutzer
First submitted to arxiv on: 20 Sep 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 ByT5-Sanskrit is a new, highly performing language model designed specifically for processing Sanskrit, a notoriously challenging morphologically rich language. The model surpasses previous data-driven approaches by a significant margin in established Sanskrit word segmentation tasks and matches the performance of the current best lexicon-based model. ByT5-Sanskrit also achieves state-of-the-art results in Vedic Sanskrit dependency parsing and OCR post-correction tasks, showcasing its robustness to unseen data. Furthermore, the paper introduces a novel multitask dataset for joint training of Sanskrit word segmentation, lemmatization, and morphosyntactic tagging tasks, fine-tuning ByT5-Sanskrit on this dataset to create a versatile multitask model for various downstream Sanskrit applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ByT5-Sanskrit is a special computer program that helps process the ancient Indian language Sanskrit. This language is very hard to work with because it has lots of tiny changes in words and meanings. The new model does a great job of breaking down words into their parts, which is important for tasks like understanding texts or translating them. It’s also good at figuring out how words relate to each other in sentences. Plus, it can even help correct mistakes made by computers when reading handwritten Sanskrit texts! |
Keywords
» Artificial intelligence » Dependency parsing » Fine tuning » Language model » Lemmatization
