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Arturo Oncevay

7 accepted papers

2025

AfroCS-xs: Creating a Compact, High-Quality, Human-Validated Code-Switched Dataset for African Languages

Kayode Olaleye, Arturo Oncevay, Mathieu Sibue, Nombuyiselo Zondi, Michelle Terblanche, Sibongile Mapikitla +3
ACL 2025long

Code-switching is prevalent in multilingual communities but lacks adequate high-quality data for model development, especially for African languages. To address this, we present AfroCS-xs, a small human-validated synthetic code-switched dataset for four African languages (Afrikaans, Sesotho, Yoruba,…

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2025

The Impact of Domain-Specific Terminology on Machine Translation for Finance in European Languages

Arturo Oncevay, Charese Smiley, Xiaomo Liu
NAACL 2025long

Domain-specific machine translation (MT) poses significant challenges due to specialized terminology, particularly when translating across multiple languages with scarce resources. In this study, we present the first impact analysis of domain-specific terminology on multilingual MT for finance, focu…

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2025

Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education

Arturo Oncevay, Elena Kochkina, Keshav Ramani, Toyin Aguda, Simerjot Kaur, Charese Smiley
EMNLP 2025

Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically d

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2022

AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan +11
ACL 2022long

Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot l…

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2022

BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages

Manuel Mager, Arturo Oncevay, Elisabeth Mager, Katharina Kann, Thang Vu
ACL 2022findings

Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation. We investigate a wide variety of supervised and unsupervised morphological segmentation methods for four polysynthetic l…

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2022

Quantifying Synthesis and Fusion and their Impact on Machine Translation

Arturo Oncevay, Duygu Ataman, Niels Van Berkel, Barry Haddow, Alexandra Birch, Johannes Bjerva
NAACL 2022long

Theoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we…

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2022

Revisiting Syllables in Language Modelling and Their Application on Low-Resource Machine Translation

Arturo Oncevay, Kervy Dante Rivas Rojas, Liz Karen Chavez Sanchez, Roberto Zariquiey
COLING 2022main

Language modelling and machine translation tasks mostly use subword or character inputs, but syllables are seldom used. Syllables provide shorter sequences than characters, require less-specialised extracting rules than morphemes, and their segmentation is not impacted by the corpus size. In this st…

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