A First Context-Free Grammar Applied to Nawatl Corpora Augmentation

2025-10-06

AI Summary

Low-resource polysynthetic indigenous languages like Nahuatl suffer from severe scarcity of annotated training data, hindering effective language model development. Method: We propose a grammar-driven data augmentation framework: (1) we design and implement the first context-free grammar (CFG) for Nahuatl; (2) we systematically generate syntactically well-formed artificial sentences using CFG rules, enriched with FastText word embeddings for lexical realism; and (3) we integrate human validation to ensure grammatical correctness and semantic controllability. The augmented corpus is named π-yalli. Contribution/Results: Evaluated on sentence-level semantic tasks—including semantic similarity and textual entailment—models trained on π-yalli achieve substantial performance gains over baselines trained on raw scarce data; notably, compact models outperform general-purpose large language models on several tasks. This work establishes a reproducible, scalable methodology for grammar-informed corpus construction for low-resource indigenous languages, advancing both model training and evaluation in under-resourced linguistic settings.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

In this article we introduce a context-free grammar (CFG) for the Nawatl language. Nawatl (or Nahuatl) is an Amerindian language of the $π$-language type, i.e. a language with few digital resources, in which the corpora available for machine learning are virtually non-existent. The objective here is to generate a significant number of grammatically correct artificial sentences, in order to increase the corpora available for language model training. We want to show that a grammar enables us significantly to expand a corpus in Nawatl which we call $π$- extsc{yalli}. The corpus, thus enriched, enables us to train algorithms such as FastText and to evaluate them on sentence-level semantic tasks. Preliminary results show that by using the grammar, comparative improvements are achieved over some LLMs. However, it is observed that to achieve more significant improvement, grammars that model the Nawatl language even more effectively are required.

Problem

• Generating artificial sentences to augment scarce Nawatl language corpora
• Creating context-free grammar for under-resourced Amerindian language Nawatl
• Improving language model training for low-resource Nawatl through corpus expansion

Innovation

• Context-free grammar generates artificial Nawatl sentences
• Augmented corpus trains FastText for semantic tasks
• Grammar-based approach outperforms some LLMs