Publication
Enhancing accessible communication: from European Portuguese to Portuguese Sign Language
| dc.contributor.author | Sousa, Catarina | |
| dc.contributor.author | Coheur, Luísa | |
| dc.contributor.author | Moita, Mara | |
| dc.date.accessioned | 2024-02-07T10:29:36Z | |
| dc.date.available | 2024-02-07T10:29:36Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Portuguese Sign Language (LGP) is the official language in deaf education in Portugal. Current approaches in developing a translation system between European Portuguese and LGP rely on hand-crafted rules. In this paper, we present a fully automatic corpora-driven rule-based machine translation system between European Portuguese and LGP glosses, and also two neural machine translation models. We also contribute with the LGP-5-Domain corpus, composed of five different text domains, built with the help of our rule-based system, and used to train the neural models. In addition, we provide a gold collection, annotated by LGP experts, that can be used for future evaluations. Compared with the only similar available translation system, PE2LGP, results are always improved with the new rule-based model, which competes for the highest scores with one of the neural models. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.18653/v1/2023.findings-emnlp.766 | pt_PT |
| dc.identifier.eid | 85183295049 | |
| dc.identifier.isbn | 9781959429470 | |
| dc.identifier.isbn | 9798891760615 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/43860 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Association for Computational Linguistics (ACL) | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.title | Enhancing accessible communication: from European Portuguese to Portuguese Sign Language | pt_PT |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | United States | pt_PT |
| oaire.citation.endPage | 11460 | pt_PT |
| oaire.citation.startPage | 11452 | pt_PT |
| oaire.citation.title | Findings of the Association for Computational Linguistics: EMNLP 2023 | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | bookPart | pt_PT |