Identifying Literary Characters in Portuguese

Santos, Diana; Willrich, Roberto; Langfeldt, Marcia Caetano; Moraes, Ricardo Gaiotto de; Mota, Cristina; Assis, Emanoel Cesar Pires de; Schumacher, Rebeca; Pereira, Paulo Silva

doi:10.1007/978-3-030-98305-5_39

Cited by 2 publications

(1 citation statement)

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“…Literary Texts Selected works found in the DIP collection 4 from Linguateca 5 resource center. DIP is a shared task whose goal is to identify of characters and respective attributes in literary works (Santos et al, 2022). In order to avoid examples with an orthography that might be too different from modern Portuguese, we have only selected works released after 1910.…”

Section: Datasetsmentioning

confidence: 99%

A Rewriting Approach for Gender Inclusivity in Portuguese

Veloso,

Coheur,

Ribeiro

2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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In recent years, there has been a notable rise in research interest regarding the integration of gender-inclusive and gender-neutral language in natural language processing models. A specific area of focus that has gained practical and academic significant interest is gender-neutral rewriting, which involves converting binarygendered text to its gender-neutral counterpart. However, current approaches to gender-neutral rewriting for gendered languages tend to rely on large datasets, which may not be an option for languages with fewer resources, such as Portuguese. In this paper, we present a rule-based and a neural-based tool for genderneutral rewriting for Portuguese, a heavily gendered Romance language whose morphology creates different challenges from the ones tackled by other gender-neutral rewriters. Our neural approach relies on fine-tuning large multilingual machine translation models on examples generated by the rule-based model. We evaluate both models on texts from different sources and contexts. We provide the first Portuguese dataset explicitly containing gender-neutral language and neopronouns, as well as a manually annotated golden collection of 500 sentences that allows for evaluation of future work.

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Section: Datasetsmentioning

confidence: 99%