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.
@article{arxiv.2508.15836,
title = {MorphNAS: Differentiable Architecture Search for Morphologically-Aware Multilingual NER},
author = {Prathamesh Devadiga and Omkaar Jayadev Shetty and Hiya Nachnani and Prema R},
journal= {arXiv preprint arXiv:2508.15836},
year = {2025}
}