| Autor(es): |
Cunha, Rossana
;
Chinonso, Osuji Cynthia
;
Moura Campos, Joao Gabriel
;
Timoney, Brian
;
Davis, Brian
;
Cozman, Fabio
;
Pagano, Adriana
;
Ferreira, Thiago Castro
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF THE FIFTH WORKSHOP ON INSIGHTS FROM NEGATIVE RESULTS IN NLP; v. N/A, p. 9-pg., 2024-01-01. |
| Resumo | |
Neural end-to-end surface realizers output more fluent texts than classical architectures. However, they tend to suffer from adequacy problems, in particular hallucinations in numerical referring expression generation. This poses a problem to language generation in sensitive domains, as is the case of robot journalism covering COVID-19 and Amazon deforestation. We propose an approach whereby numerical referring expressions are converted from digits to plain word form descriptions prior to being fed to state-of-the-art Large Language Models. We conduct automatic and human evaluations to report the best strategy to numerical superficial realization. Code and data are publicly available. (AU) | |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada |