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Juan Antonio Pérez-Ortiz

5 accepted papers

2025

Beyond the Mode: Sequence-Level Distillation of Multilingual Translation Models for Low-Resource Language Pairs

Aarón Galiano-Jiménez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena
NAACL 2025findings

This paper delves into sequence-level knowledge distillation (KD) of multilingual pre-trained translation models. We posit that, beyond the approximated mode obtained via beam search, the whole output distribution of the teacher contains valuable insights for students. We explore the potential of n-…

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2024

Curated Datasets and Neural Models for Machine Translation of Informal Registers between Mayan and Spanish Vernaculars

Andrés Lou, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Víctor Sánchez-Cartagena
NAACL 2024long

The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure. In this paper we develop, curate, and publicly release a set of corpora in severa…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2022

Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation

Miquel Esplà-Gomis, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
EMNLP 2022main

Computer-aided translation (CAT) tools based on translation memories (MT) play a prominent role in the translation workflow of professional translators. However, the reduced availability of in-domain TMs, as compared to in-domain monolingual corpora, limits its adoption for a number of translation t…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2021

Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach

Víctor M. Sánchez-Cartagena, Miquel Esplà-Gomis, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
EMNLP 2021main

In the context of neural machine translation, data augmentation (DA) techniques may be used for generating additional training samples when the available parallel data are scarce. Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2020

Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation

Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
COLING 2020main

This paper studies the effects of word-level linguistic annotations in under-resourced neural machine translation, for which there is incomplete evidence in the literature. The study covers eight language pairs, different training corpus sizes, two architectures, and three types of annotation: dummy…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗