# MorphNAS: Differentiable Architecture Search for Morphologically-Aware Multilingual NER

arXiv: https://arxiv.org/abs/2508.15836 · HTML: https://mlsanity.com/paper/2508.15836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [sharpDARTS: Faster and More Accurate Differentiable Architecture Search](https://mlsanity.com/paper/1903.09900/) (2019)
- [DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation](https://mlsanity.com/paper/2005.07029/) (2020)
- [Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures](https://mlsanity.com/paper/2106.07211/) (2021)

## Abstract (arXiv)

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.