Papers by Arturo Oncevay
Quantifying Synthesis and Fusion and their Impact on Machine Translation (2022.naacl-main)
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| Challenge: | Literature in Natural Language Processing (NLP) typically labels whole language with strict type of morphology, e.g. fusional or agglutinative. |
| Approach: | They propose to quantify morphological typology at the word and segment level by using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). |
| Outcome: | The proposed method reduces the rigidity of NLP classification claims by measuring morphological diversity at the word and segment level. |
Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo (2022.lrec-1)
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Roberto Zariquiey, Claudia Alvarado, Ximena Echevarría, Luisa Gomez, Rosa Gonzales, Mariana Illescas, Sabina Oporto, Frederic Blum, Arturo Oncevay, Javier Vera
| Challenge: | 12 This paper describes the collaborative methodology implemented to create a UD treebank for a Peruvian endangered language. |
| Approach: | They propose to create a UD treebank for a Peruvian endangered language . they use a collaborative methodology to create the treebank in a course . |
| Outcome: | The proposed treebank would enhance the future development of an NLP toolkit for this endangered language. |
Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)
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| Challenge: | Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings. |
| Approach: | They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
| Outcome: | The proposed terminology resource covers seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
AfroCS-xs: Creating a Compact, High-Quality, Human-Validated Code-Switched Dataset for African Languages (2025.acl-long)
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Kayode Olaleye, Arturo Oncevay, Mathieu Sibue, Nombuyiselo Zondi, Michelle Terblanche, Sibongile Mapikitla, Richard Lastrucci, Charese Smiley, Vukosi Marivate
| Challenge: | AfroCS-xs is a low-quality dataset for code-switching in multilingual communities . code-witching is prevalent in multicultural societies but lacks high-quality data for model development . |
| Approach: | They propose to use human-validated synthetic code-switched datasets to generate code-witched sentences for four African languages and English within a specific domain—agriculture. |
| Outcome: | The proposed model improves translation accuracy on the high-quality dataset for four African languages and English within a specific domain—agriculture. |
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages (2022.acl-long)
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Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann
| Challenge: | Pretrained multilingual models can perform cross-lingual transfer in a zero-shot setting, even for unseen languages. |
| Approach: | They propose to extend XNLI to 10 indigenous languages of the Americas and test multiple zero-shot and translation-based approaches. |
| Outcome: | The proposed model can perform cross-lingual transfer in a zero-shot setting even for languages unseen during pretraining. |
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru (2020.lrec-1)
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| Challenge: | Existing methods for extracting text from PDF files are expensive and limited by the absence of web content of endangered languages. |
| Approach: | They propose a method for creating monolingual corpora for four endangered languages . they use a PDF file format with multilingual sentences and noisy pages . |
| Outcome: | The proposed method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays. |
SchAman: Spell-Checking Resources and Benchmark for Endangered Languages from Amazonia (2022.aacl-short)
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Arturo Oncevay, Gerardo Cardoso, Carlo Alva, César Lara Ávila, Jovita Vásquez Balarezo, Saúl Escobar Rodríguez, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Juan López Bautista, Nimia Acho Rios, Remigio Zapata Cesareo, Héctor Erasmo Gómez Montoya, Roberto Zariquiey
| Challenge: | Spell-checking as a generation task requires large amount of data, which is not feasible for endangered languages such as the languages spoken in Peru. |
| Approach: | They propose to use augmented misspelling data to train neural spell-checking models for four endangered languages of Peru: Shipibo-Koniba, Asháninka, Yánesha, yine . |
| Outcome: | The proposed model achieves better scores in most of the errors and languages in the four indigenous languages of Peru: Shipibo-Koniba, Asháninka, Yánesha, yine. |
ChAnot: An Intelligent Annotation Tool for Indigenous and Highly Agglutinative Languages in Peru (L18-1)
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| Challenge: | Linguistic corpus annotation is one of the most important phases for addressing natural language processing (NLP) tasks. |
| Approach: | They propose a web-based annotation tool for Peruvian indigenous and highly agglutinative languages that supports a variety of linguistic annotation tasks. |
| Outcome: | The proposed tool supports a diverse set of linguistic annotation tasks, such as morphological segmentation markup, POS-tag markup and other. |
Distill and Align Decomposition for Enhanced Claim Verification (2026.findings-eacl)
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Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay, Charese Smiley, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing methods for complex claim verification struggle to align decomposition quality with verification performance. |
| Approach: | They propose a reinforcement learning approach that optimizes decomposition quality and verifier alignment using Group Relative Policy Optimization. |
| Outcome: | The proposed method outperforms prompt-based approaches and existing methods in six evaluation settings. |
Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks (2020.acl-main)
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| Challenge: | Hierarchical text classification is a complex task that requires extended training time and a large number of parameters. |
| Approach: | They propose a top-up-classification task using dictionaries and auxiliary task from external dictionary definitions. |
| Outcome: | The proposed method outperforms previous studies using a reduced number of parameters in two well-known English datasets. |
Exploring Enhanced Code-Switched Noising for Pretraining in Neural Machine Translation (2023.findings-eacl)
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| Challenge: | Multilingual pretraining approaches to denoise synthetic code-switched data have shown that they generate the noise using non-contextual, one-to-one word translations obtained from lexicons. |
| Approach: | They propose an approach where contextual, many-to-many word translations are generated using a ‘base’ NMT model. |
| Outcome: | The proposed approach improves on 3 different language families and shows that small models can perform better than massive models like mBART50 and mRASP2 . |
Revisiting Syllables in Language Modelling and Their Application on Low-Resource Machine Translation (2022.coling-1)
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| Challenge: | Language modelling and machine translation tasks mostly use subword or character inputs, but syllables are rarely used. |
| Approach: | They explore the potential of syllables for open-vocabulary language modelling in 21 languages. |
| Outcome: | The proposed method outperforms characters and subwords in a non-related and low-resource language pair. |
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages (2022.findings-acl)
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| Challenge: | Morphologically rich polysynthetic languages present a challenge for NLP systems due to data sparsity. |
| Approach: | They propose to use subword segmentation to reduce data sparsity in polysynthetic languages . they compare supervised and unsupervised morphological segmentation methods to Byte-Pair Encodings . |
| Outcome: | The proposed methods outperform BPEs in MT tasks for all language pairs except for Nahuatl . the proposed methods are more efficient than supervised methods, but less sparse in fusional languages. |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
The Impact of Domain-Specific Terminology on Machine Translation for Finance in European Languages (2025.naacl-long)
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| Challenge: | Existing datasets for evaluating MT systems in this domain are limited. |
| Approach: | They propose to use a multi-parallel corpus from the European Central Bank to analyze the impact of domain-specific terminology on multilingual machine translation for finance. |
| Outcome: | The proposed method compares open-source multilingual MT systems with large language models (LLMs) that possess multilingual capabilities. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
| Challenge: | Large multilingual models have inspired a new class of word alignment methods, which work well for pretraining languages. |
| Approach: | They propose to use transformer-based word alignment methods to extract alignments from massive pretrained models. |
| Outcome: | The proposed methods outperform traditional methods for languages unseen to pretraining models, and are competitive with each other. |
Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations (2020.emnlp-main)
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| Challenge: | Recent studies consider linguistic typology as a potential source of knowledge to support multilingual natural language processing (NLP) tasks. |
| Approach: | They propose to fuse both views using canonical correlation analysis and use it to infer typological features and language phylogenies to construct a multi-view language vector space for multilingual machine translation. |
| Outcome: | The proposed model achieves competitive translation accuracy in multilingual machine translation tasks without expensive retraining of massive multilingual or ranking models. |