John E. Ortega

Also published as: John E Ortega


2022

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The Nós Project: Opening routes for the Galician language in the field of language technologies
Iria de-Dios-Flores | Carmen Magariños | Adina Ioana Vladu | John E. Ortega | José Ramom Pichel | Marcos García | Pablo Gamallo | Elisa Fernández Rei | Alberto Bugarín-Diz | Manuel González González | Senén Barro | Xosé Luis Regueira
Proceedings of the Workshop Towards Digital Language Equality within the 13th Language Resources and Evaluation Conference

The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a driving force in the democratization of the communities that use them due to their great social and cultural impact. As an example, dialogue systems allow us to communicate with machines in our own language; machine translation increases access to contents in different languages, thus facilitating intercultural relations; and text-to-speech and speech-to-text systems broaden different categories of users’ access to technology. In the case of Galician (co-official language, together with Spanish, in the autonomous region of Galicia, located in northwestern Spain), incorporating the language into state-of-the-art AI applications can not only significantly favor its prestige (a decisive factor in language normalization), but also guarantee citizens’ language rights, reduce social inequality, and narrow the digital divide. This is the main motivation behind the Nós Project (Proxecto Nós), which aims to have a significant contribution to the development of LTs in Galician (currently considered a low-resource language) by providing openly licensed resources, tools, and demonstrators in the area of intelligent technologies.

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Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for Machine Translation)
John E. Ortega | Marine Carpuat | William Chen | Katharina Kann | Constantine Lignos | Maja Popovic | Shabnam Tafreshi
Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 2: Corpus Generation and Corpus Augmentation for Machine Translation)

2021

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Love Thy Neighbor: Combining Two Neighboring Low-Resource Languages for Translation
John E. Ortega | Richard Alexander Castro Mamani | Jaime Rafael Montoya Samame
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)

Low-resource languages sometimes take on similar morphological and syntactic characteristics due to their geographic nearness and shared history. Two low-resource neighboring languages found in Peru, Quechua and Ashaninka, can be considered, at first glance, two languages that are morphologically similar. In order to translate the two languages, various approaches have been taken. For Quechua, neural machine transfer-learning has been used along with byte-pair encoding. For Ashaninka, the language of the two with fewer resources, a finite-state transducer is used to transform Ashaninka texts and its dialects for machine translation use. We evaluate and compare two approaches by attempting to use newly-formed Ashaninka corpora for neural machine translation. Our experiments show that combining the two neighboring languages, while similar in morphology, word sharing, and geographical location, improves Ashaninka– Spanish translation but degrades Quechua–Spanish translations.

2020

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Overcoming Resistance: The Normalization of an Amazonian Tribal Language
John E Ortega | Richard Alexander Castro-Mamani | Jaime Rafael Montoya Samame
Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages

Languages can be considered endangered for many reasons. One of the principal reasons for endangerment is the disappearance of its speakers. Another, more identifiable reason, is the lack of written resources. We present an automated sub-segmentation system called AshMorph that deals with the morphology of an Amazonian tribal language called Ashaninka which is at risk of being endangered due to the lack of availability (or resistance) of native speakers and the absence of written resources. We show that by the use of a cross-lingual lexicon and finite state transducers we can increase accuracy by more than 30% when compared to other modern sub-segmentation tools. Our results, made freely available on-line, are verified by an Ashaninka speaker and perform well in two distinct domains, everyday literary articles and the bible. This research serves as a first step in helping to preserve Ashaninka by offering a sub-segmentation process that can be used to normalize any Ashaninka text which will serve as input to a machine translation system for translation into other high-resource languages spoken by higher populated locations like Spanish and Portuguese in the case of Peru and Brazil where Ashaninka is mostly spoken.

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Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation
John E. Ortega | Marcello Federico | Constantin Orasan | Maja Popovic
Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation

2014

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Using any machine translation source for fuzzy-match repair in a computer-aided translation setting
John E. Ortega | Felipe Sánchez-Martinez | Mikel L. Forcada
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

When a computer-assisted translation (CAT) tool does not find an exact match for the source segment to translate in its translation memory (TM), translators must use fuzzy matches that come from translation units in the translation memory that do not completely match the source segment. We explore the use of a fuzzy-match repair technique called patching to repair translation proposals from a TM in a CAT environment using any available machine translation system, or any external bilingual source, regardless of its internals. Patching attempts to aid CAT tool users by repairing fuzzy matches and proposing improved translations. Our results show that patching improves the quality of translation proposals and reduces the amount of edit operations to perform, especially when a specific set of restrictions is applied.