Carlo Alva


2022

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SchAman: Spell-Checking Resources and Benchmark for Endangered Languages from Amazonia
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
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Spell-checkers are core applications in language learning and normalisation, which may enormously contribute to language revitalisation and language teaching in the context of indigenous communities. Spell-checking as a generation task, however, requires large amount of data, which is not feasible for endangered languages, such as the languages spoken in Peruvian Amazonia. We propose here augmentation methods for various misspelling types as a strategy to train neural spell-checking models and we create an evaluation resource for four indigenous languages of Peru: Shipibo-Konibo, Asháninka, Yánesha, Yine. We focus on special errors that are significant for learning these languages, such as phoneme-to-grapheme ambiguity, grammatical errors (gender, tense, number, among others), accentuation, punctuation and normalisation in contexts where two or more writing traditions co-exist. We found that an ensemble model, trained with augmented data from various types of error achieves overall better scores in most of the error types and languages. Finally, we released our spell-checkers as a web service to be used by indigenous communities and organisations to develop future language materials.

2017

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Spell-Checking based on Syllabification and Character-level Graphs for a Peruvian Agglutinative Language
Carlo Alva | Arturo Oncevay
Proceedings of the First Workshop on Subword and Character Level Models in NLP

There are several native languages in Peru which are mostly agglutinative. These languages are transmitted from generation to generation mainly in oral form, causing different forms of writing across different communities. For this reason, there are recent efforts to standardize the spelling in the written texts, and it would be beneficial to support these tasks with an automatic tool such as an spell-checker. In this way, this spelling corrector is being developed based on two steps: an automatic rule-based syllabification method and a character-level graph to detect the degree of error in a misspelled word. The experiments were realized on Shipibo-konibo, a highly agglutinative and amazonian language, and the results obtained have been promising in a dataset built for the purpose.