Abstract
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.
Abstract (translated)
形态复杂的语言，尤其是多文种的印度语系语言，在自然语言处理（NLP）中面临着重大挑战。本文介绍了MorphNAS，这是一种新颖的可微神经架构搜索框架，旨在解决这些挑战。MorphNAS通过引入诸如书写系统类型和形态复杂性等语言元特征来增强可微架构搜索（DARTS），以优化针对命名实体识别（NER）任务的神经网络结构。它能够自动识别出最适合特定语言词形变化特点的小规模架构元素。通过自动化这一搜索过程，MorphNAS旨在最大化多语种NLP模型的专业能力，从而提高对这些复杂语言的理解和处理效率。
URL
https://arxiv.org/abs/2508.15836
