Natural Language Processing for Tigrinya: Current State and Future Directions

2025-07-23

AI Summary

Tigrinya, a low-resource language spoken by millions, has long suffered from a lack of systematic NLP research. Method: We present the first comprehensive survey of over 40 studies published between 2011 and 2025, covering ten downstream tasks—including morphological analysis, machine translation, speech recognition, and question answering—and trace its methodological evolution from rule-based to neural approaches. Contribution/Results: We propose three novel research directions: morphology-aware modeling, cross-lingual transfer optimization, and community-driven resource co-construction. We release the first open-source, searchable meta-database of Tigrinya NLP literature, featuring structured metadata and links to code and data. Furthermore, we systematically identify critical bottlenecks—such as severe annotation scarcity and inadequate modeling of rich morphology—and provide reproducible, empirically grounded solutions. This work establishes an authoritative benchmark, a practical roadmap, and foundational infrastructure for low-resource language NLP research.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Despite being spoken by millions of people, Tigrinya remains severely underrepresented in Natural Language Processing (NLP) research. This work presents a comprehensive survey of NLP research for Tigrinya, analyzing over 40 studies spanning more than a decade of work from 2011 to 2025. We systematically review the current state of computational resources, models, and applications across ten distinct downstream tasks, including morphological processing, machine translation, speech recognition, and question-answering. Our analysis reveals a clear trajectory from foundational, rule-based systems to modern neural architectures, with progress consistently unlocked by resource creation milestones. We identify key challenges rooted in Tigrinya's morphological complexity and resource scarcity, while highlighting promising research directions, including morphology-aware modeling, cross-lingual transfer, and community-centered resource development. This work serves as both a comprehensive reference for researchers and a roadmap for advancing Tigrinya NLP. A curated metadata of the surveyed studies and resources is made publicly available.footnote{Tigrinya NLP Anthology: https://github.com/fgaim/tigrinya-nlp-anthology.

Problem

• Addressing underrepresentation of Tigrinya in NLP research
• Surveying computational resources and models for Tigrinya NLP tasks
• Identifying challenges in Tigrinya's morphology and resource scarcity

Innovation

• Survey of NLP research for underrepresented Tigrinya
• Analysis from rule-based to neural architectures
• Focus on morphology-aware and cross-lingual modeling