Papers by Sebastian Nehrdich
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks (D18-1)
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| Challenge: | Using end-to-end neural network models, Sanskrit is tokenized by splitting compounds and resolving phonetic merges. |
| Approach: | They propose end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges. |
| Outcome: | The proposed models outperform the state-of-the-art for the task of splitting compounds and resolving phonetic merges. |
SansTib, a Sanskrit - Tibetan Parallel Corpus and Bilingual Sentence Embedding Model (2022.lrec-1)
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| Challenge: | a large digital monolingual corpus of Sanskrit and Tibetan Buddhist literature has become available. |
| Approach: | They propose to develop a Sanskrit - Classical Tibetan parallel corpus automatically aligned on sentence-level and a bilingual sentence embedding model. |
| Outcome: | The proposed model improves the existing Sanskrit - Classical Tibetan parallel corpus and its bilingual sentence embedding model. |
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks (2024.findings-emnlp)
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| Challenge: | Morphologically rich languages are notoriously challenging to process for downstream NLP applications. |
| Approach: | They propose a pretrained model for NLP applications involving the morphologically rich language Sanskrit that outperforms previous models by a considerable margin. |
| Outcome: | The proposed model outperforms tokenized models on established Sanskrit word segmentation tasks and matches the current best lexicon-based model. |