Roberto Zariquiey


2024

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Unlocking Knowledge with OCR-Driven Document Digitization for Peruvian Indigenous Languages
Shadya Sanchez Carrera | Roberto Zariquiey | Arturo Oncevay
Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)

The current focus on resource-rich languages poses a challenge to linguistic diversity, affecting minority languages with limited digital presence and relatively old published and unpublished resources. In addressing this issue, this study targets the digitalization of old scanned textbooks written in four Peruvian indigenous languages (Asháninka, Shipibo-Konibo, Yanesha, and Yine) using Optical Character Recognition (OCR) technology. This is complemented with text correction methods to minimize extraction errors. Contributions include the creation of an annotated dataset with 454 scanned page images, for a rigorous evaluation, and the development of a module to correct OCR-generated transcription alignments.

2022

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UniMorph 4.0: Universal Morphology
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
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

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Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo
Roberto Zariquiey | Claudia Alvarado | Ximena Echevarría | Luisa Gomez | Rosa Gonzales | Mariana Illescas | Sabina Oporto | Frederic Blum | Arturo Oncevay | Javier Vera
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we launch a new Universal Dependencies treebank for an endangered language from Amazonia: Kakataibo, a Panoan language spoken in Peru. We first discuss the collaborative methodology implemented, which proved effective to create a treebank in the context of a Computational Linguistic course for undergraduates. Then, we describe the general details of the treebank and the language-specific considerations implemented for the proposed annotation. We finally conduct some experiments on part-of-speech tagging and syntactic dependency parsing. We focus on monolingual and transfer learning settings, where we study the impact of a Shipibo-Konibo treebank, another Panoan language resource.

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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.

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CLD² Language Documentation Meets Natural Language Processing for Revitalising Endangered Languages
Roberto Zariquiey | Arturo Oncevay | Javier Vera
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

Language revitalisation should not be understood as a direct outcome of language documentation, which is mainly focused on the creation of language repositories. Natural language processing (NLP) offers the potential to complement and exploit these repositories through the development of language technologies that may contribute to improving the vitality status of endangered languages. In this paper, we discuss the current state of the interaction between language documentation and computational linguistics, present a diagnosis of how the outputs of recent documentation projects for endangered languages are underutilised for the NLP community, and discuss how the situation could change from both the documentary linguistics and NLP perspectives. All this is introduced as a bridging paradigm dubbed as Computational Language Documentation and Development (CLD²). CLD² calls for (1) the inclusion of NLP-friendly annotated data as a deliverable of future language documentation projects; and (2) the exploitation of language documentation databases by the NLP community to promote the computerization of endangered languages, as one way to contribute to their revitalization.

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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
Proceedings of the 29th International Conference on Computational Linguistics

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 study, we first explore the potential of syllables for open-vocabulary language modelling in 21 languages. We use rule-based syllabification methods for six languages and address the rest with hyphenation, which works as a syllabification proxy. With a comparable perplexity, we show that syllables outperform characters and other subwords. Moreover, we study the importance of syllables on neural machine translation for a non-related and low-resource language-pair (Spanish–Shipibo-Konibo). In pairwise and multilingual systems, syllables outperform unsupervised subwords, and further morphological segmentation methods, when translating into a highly synthetic language with a transparent orthography (Shipibo-Konibo). Finally, we perform some human evaluation, and discuss limitations and opportunities.

2020

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No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru
Gina Bustamante | Arturo Oncevay | Roberto Zariquiey
Proceedings of the Twelfth Language Resources and Evaluation Conference

We introduce new monolingual corpora for four indigenous and endangered languages from Peru: Shipibo-konibo, Ashaninka, Yanesha and Yine. Given the total absence of these languages in the web, the extraction and processing of texts from PDF files is relevant in a truly low-resource language scenario. Our procedure for monolingual corpus creation considers language-specific and language-agnostic steps, and focuses on educational PDF files with multilingual sentences, noisy pages and low-structured content. Through an evaluation based on language modelling and character-level perplexity on a subset of manually extracted sentences, we determine that our method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays.

2018

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Toward Universal Dependencies for Shipibo-Konibo
Alonso Vasquez | Renzo Ego Aguirre | Candy Angulo | John Miller | Claudia Villanueva | Željko Agić | Roberto Zariquiey | Arturo Oncevay
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

We present an initial version of the Universal Dependencies (UD) treebank for Shipibo-Konibo, the first South American, Amazonian, Panoan and Peruvian language with a resource built under UD. We describe the linguistic aspects of how the tagset was defined and the treebank was annotated; in addition we present our specific treatment of linguistic units called clitics. Although the treebank is still under development, it allowed us to perform a typological comparison against Spanish, the predominant language in Peru, and dependency syntax parsing experiments in both monolingual and cross-lingual approaches.
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