Papers by Víctor M. Sánchez-Cartagena
Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation (2020.coling-main)
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| Challenge: | Using word-level linguistic annotations in under-resourced neural machine translation is challenging for many languages. |
| Approach: | They propose to use word-level linguistic annotations to label source-language (SL) or target-language words to improve translation performance. |
| Outcome: | The proposed language annotations outperform part of speech and morphological description tags in the target language, while the morpho-syntactic description tags improve the grammaticality of the output. |
Beyond the Mode: Sequence-Level Distillation of Multilingual Translation Models for Low-Resource Language Pairs (2025.findings-naacl)
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Aarón Galiano-Jiménez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena
| Challenge: | Existing multilingual pre-trained models for low-resource languages have outperformed those trained from scratch for low resources due to high hardware requirements. |
| Approach: | They propose to use beam search to decode the whole output distribution of the teacher to improve student learning. |
| Outcome: | The proposed methods improve student model performance and reduce gender bias amplification common to beam search based methods. |
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation (2022.emnlp-main)
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| Challenge: | CAT tools based on translation memories (TMs) are limited in their use for a number of translation tasks due to the limited availability of in-domain TMs. |
| Approach: | They propose a neural approach to exploit in-domain TMs and in-target-language (TL) monolingual corpora to exploit CAT tools. |
| Outcome: | The proposed approach exploits in-domain TMs and in-target-language (TL) monolingual corpora and increases translation proposals on four language pairs. |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
| Approach: | They propose to use data augmentation techniques to generate additional parallel sentences by reversing the order of the target sentence to produce unfluent target sentences. |
| Outcome: | The proposed approach improves on six low-resource translation tasks and the baseline and over DA methods. |