Oscar M. Cumbicus-Pineda

Also published as: Oscar m. Cumbicus-Pineda


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IrekiaLFes: a New Open Benchmark and Baseline Systems for Spanish Automatic Text Simplification
Itziar Gonzalez-Dios | Iker Gutiérrez-Fandiño | Oscar m. Cumbicus-Pineda | Aitor Soroa
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

Automatic Text simplification (ATS) seeks to reduce the complexity of a text for a general public or a target audience. In the last years, deep learning methods have become the most used systems in ATS research, but these systems need large and good quality datasets to be evaluated. Moreover, these data are available on a large scale only for English and in some cases with restrictive licenses. In this paper, we present IrekiaLF_es, an open-license benchmark for Spanish text simplification. It consists of a document-level corpus and a sentence-level test set that has been manually aligned. We also conduct a neurolinguistically-based evaluation of the corpus in order to reveal its suitability for text simplification. This evaluation follows the Lexicon-Unification-Linearity (LeULi) model of neurolinguistic complexity assessment. Finally, we present a set of experiments and baselines of ATS systems in a zero-shot scenario.


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A Syntax-Aware Edit-based System for Text Simplification
Oscar M. Cumbicus-Pineda | Itziar Gonzalez-Dios | Aitor Soroa
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Edit-based text simplification systems have attained much attention in recent years due to their ability to produce simplification solutions that are interpretable, as well as requiring less training examples compared to traditional seq2seq systems. Edit-based systems learn edit operations at a word level, but it is well known that many of the operations performed when simplifying text are of a syntactic nature. In this paper we propose to add syntactic information into a well known edit-based system. We extend the system with a graph convolutional network module that mimics the dependency structure of the sentence, thus giving the model an explicit representation of syntax. We perform a series of experiments in English, Spanish and Italian, and report improvements of the state of the art in four out of five datasets. Further analysis shows that syntactic information is always beneficial, and suggest that syntax is more helpful in complex sentences.