Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya
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Computer Science > Computation and Language
arXiv:2607.15209 (cs)
[Submitted on 16 Jul 2026]
Title:Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya
View a PDF of the paper titled Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya, by Hailay Kidu Teklehaymanot and 2 other authors
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Abstract:Multilingual pre-trained language models (PLMs) exhibit degraded performance on low-resource, non-Latin-script languages, driven by high out-of-vocabulary (OOV) rates and excessive subword fragmentation that result from Latin-script-centric tokenizer training. We introduce VEXMLM, a vocabulary-extended variant of XLM-R targeting the two highest-resource Ge'ez-script languages, Amharic and Tigrinya, and further evaluated on 17 additional low-resource African languages (19 total). We train a language-specific SentencePiece tokenizer on curated Amharic and Tigrinya monolingual corpora, extend XLM-R's vocabulary with 30,000 Ge'ez-script subwords derived from this tokenizer, and initialize their embeddings by averaging the embeddings of their constituent subwords under XLM-R's original tokenizer. VEXMLM is trained in two stages: (1) continued masked language modeling over the extended vocabulary on the curated corpora, and (2) supervised fine-tuning on question answering (QA), named entity recognition (NER), and sentiment analysis (SA). On Amharic/Tigrinya QA, VEXMLM achieves 87.0 EM /90.0 F1, versus 66.0 EM/78.0 F1 for XLM-R and 74.0 EM/ 78.0 F1 for Glot500. On SA, VEXMLM reaches 80.0\% accuracy versus 77.0\% (XLM-R) and 46.0\% (Glot500). On NER, VEXMLM raises OOV-token entity accuracy from 81.4\% to 94.3\%, averaged over 11 of the 19 evaluated languages for which OOV analysis was possible. Our contributions are: (i) a vocabulary-extension and embedding-initialization procedure tailored to Ge'ez script; (ii) a two-stage training strategy under which vocabulary and continued-pretraining gains on Amharic/Tigrinya transfer to 17 typologically related, unaugmented African languages; and (iii) an evaluation spanning both intrinsic tokenization metrics (vocabulary coverage, fertility, OOV rate) and extrinsic task performance across all 19 languages.
| Comments: | 13 pages , 7 tables , 2 figurs |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.15209 [cs.CL] |
| (or arXiv:2607.15209v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15209
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Hailay Kidu Teklehaymanot [view email][v1] Thu, 16 Jul 2026 17:08:28 UTC (252 KB)
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