MorphNAS: Differentiable Architecture Search for Morphologically-Aware Multilingual NER
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912548225286144 |
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| author | Devadiga, Prathamesh Shetty, Omkaar Jayadev Nachnani, Hiya R, Prema |
| author_facet | Devadiga, Prathamesh Shetty, Omkaar Jayadev Nachnani, Hiya R, Prema |
| contents | Morphologically complex languages, particularly multiscript Indian languages, present significant challenges for Natural Language Processing (NLP). This work introduces MorphNAS, a novel differentiable neural architecture search framework designed to address these challenges. MorphNAS enhances Differentiable Architecture Search (DARTS) by incorporating linguistic meta-features such as script type and morphological complexity to optimize neural architectures for Named Entity Recognition (NER). It automatically identifies optimal micro-architectural elements tailored to language-specific morphology. By automating this search, MorphNAS aims to maximize the proficiency of multilingual NLP models, leading to improved comprehension and processing of these complex languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MorphNAS: Differentiable Architecture Search for Morphologically-Aware Multilingual NER Devadiga, Prathamesh Shetty, Omkaar Jayadev Nachnani, Hiya R, Prema Computation and Language Artificial Intelligence Machine Learning Morphologically complex languages, particularly multiscript Indian languages, present significant challenges for Natural Language Processing (NLP). This work introduces MorphNAS, a novel differentiable neural architecture search framework designed to address these challenges. MorphNAS enhances Differentiable Architecture Search (DARTS) by incorporating linguistic meta-features such as script type and morphological complexity to optimize neural architectures for Named Entity Recognition (NER). It automatically identifies optimal micro-architectural elements tailored to language-specific morphology. By automating this search, MorphNAS aims to maximize the proficiency of multilingual NLP models, leading to improved comprehension and processing of these complex languages. |
| title | MorphNAS: Differentiable Architecture Search for Morphologically-Aware Multilingual NER |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.15836 |