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O. T. Olise

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Open access Aug 2026

Dataset Curation for Kalabari NMT System

The development of Natural Language Processing (NLP) tools for endangered and low resource languages is fundamentally hindered by the scarcity of high-quality parallel data. Data for languages like Kalabari is not only scarce but often noisy, inconsistently digitized, and orthographically unstandardized. While prior work has leveraged religious texts for corpus creation, the specific challenges of extracting and normalizing morphologically rich languages with complex diacritics remain underexplored. This paper addresses this gap by introducing a reproducible, modular curation methodology tailored for such languages. We document a six-step pipeline that transforms raw digital texts—sourced from the Kalabari Bible (FiaFia Biabulu) and instructional literature (Kalabari Lingua)—into a clean, verse aligned, 10,222-pair parallel corpus. We demonstrate that enforcing Normalization Form C (NFC) is critical for preserving sub-dot diacritics, and we validate the corpus by training a baseline Transformer NMT system. Our contributions are threefold: (1) a transferable curation framework for endangered languages, (2) the first sizable Kalabari-English parallel corpus, and (3) baseline experiments that reveal both the promise and the hallucination pitfalls of training on highly constrained, domain-specific data.

O. T. Olise · 0 citations
Open access Aug 2026

Democratizing Machine Translation: A CPU-Centric Training Pipeline for Low-Resource Languages (A Kalabari Case Study)

Machine Translation (MT) systems for low-resource languages are scarce, particularly for highly divergent languages like Kalabari, a Niger-Congo language of the Ijo family. The persistent exclusion of these languages from modern language technologies is largely driven by a lack of parallel corpora, standardized tools, and the massive computational resources typically required for Neural Machine Translation (NMT). This research addresses this hardware bottleneck by detailing the end-to-end implementation of a foundational NMT system built entirely on consumer-grade CPU hardware. To establish this benchmark, a parallel corpus of 10,222 sentence pairs was manually created from available texts, carefully cleaned, and tokenized using SentencePiece Byte Pair Encoding (BPE) to mitigate morphological sparsity. A lightweight Transformer architecture was trained from scratch in OpenNMT-py on an AMD Ryzen 9 processor with 8GB RAM, utilizing SSD swap space and gradient accumulation to overcome memory limitations. The system achieved BLEU scores of 15.8 (Kalabari-to-English) and 13.5 (English-to-Kalabari), alongside promising chrF2 scores of 37.2 and 39.7 respectively. For deployment, the model was served on CPU using INT8 quantization via CTranslate2, a standard technique for reducing model size and accelerating inference on commodity hardware. The results demonstrate a reproducible, CPU-centric pipeline, proving that the lack of specialized GPU infrastructure is not an insurmountable obstacle for digital language preservation and baseline NMT development.

O. T. Olise · 0 citations