Paulo Berlanga Neto

Also published as: Paulo Berlanga Neto


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Shallow parsing of Portuguese texts annotated under Universal Dependencies
Guilherme Martiniano Oliveira | Paulo Berlanga Neto | Evandro Eduardo Seron Ruiz
Proceedings of the Universal Dependencies Brazilian Festival


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Split-and-Rephrase in a Cross-Lingual Manner: A Complete Pipeline
Paulo Berlanga Neto | Evandro Eduardo Seron Ruiz
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Split-and-rephrase is a challenging task that promotes the transformation of a given complex input sentence into multiple shorter sentences retaining equivalent meaning. This rewriting approach conceptualizes that shorter sentences benefit human readers and improve NLP downstream tasks attending as a preprocessing step. This work presents a complete pipeline capable of performing the split-and-rephrase method in a cross-lingual manner. We trained sequence-to-sequence neural models as from English corpora and applied them to predict the transformations in English and Brazilian Portuguese sentences jointly with BERT’s masked language modeling. Contrary to traditional approaches that seek training models with extensive vocabularies, we present a non-trivial way to construct symbolic ones generalized solely by grammatical classes (POS tags) and their respective recurrences, reducing the amount of necessary training data. This pipeline contribution showed competitive results encouraging the expansion of the method to languages other than English.