Linguistically enhanced text to sign gloss machine translation

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• dc.contributor.author Egea Gómez, Santiago
• dc.contributor.author Chiruzzo, Luis
• dc.contributor.author McGill, Euan
• dc.contributor.author Saggion, Horacio
• dc.date.accessioned 2022-10-17T06:20:47Z
• dc.date.available 2022-10-17T06:20:47Z
• dc.date.issued 2022
• dc.description Comunicació presentada a: 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022, celebrat del 15 al 17 de juny de 2022 a València, Espanya.
• dc.description.abstract In spite of the recent advances in Machine Translation (MT) for spoken languages, translation between spoken and Sign Languages (SLs) or between Sign Languages remains a difficult problem. Here, we study how Neural Machine Translation (NMT) might overcome the communication barriers for the Deaf and Hard-of-Hearing (DHH) community. Namely, we approach the Text2Gloss translation task in which spoken text segments are translated to lexical sign representations. In this context, we leverage transformer-based models via (1) injecting linguistic features that can guide the learning process towards better translations; and (2) applying a Transfer Learning strategy to reuse the knowledge of a pre-trained model. To this aim, different aggregation strategies are compared and evaluated under Transfer Learning and random weight initialization conditions. The results of this research reveal that linguistic features can successfully contribute to achieve more accurate models; meanwhile, the Transfer Learning procedure applied conducted to substantial performance increases.
• dc.description.sponsorship This work has been conducted within the SignON project. SignON is a Horizon 2020 project, funded under the Horizon 2020 program ICT57-2020 - "An empowering, inclusive, Next Generation Internet" with Grant Agreement number 101017255.
• dc.format.mimetype application/pdf
• dc.identifier.citation Egea Gómez S, Chiruzzo L, McGill E, Saggion H. Linguistically enhanced text to sign gloss machine translation. In: Rosso P, Basile V, Martínez R, Métais E, Meziane F, (eds). Natural Language Processing and Information Systems, 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022 Proceedings; 2022 June 15-17; Valencia, Spain. Cham: Springer; 2022. p. 172-83. DOI: 10.1007/978-3-031-08473-7_16
• dc.identifier.doi http://doi.org/10.1007/978-3-031-08473-7_16
• dc.identifier.uri http://hdl.handle.net/10230/54416
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Rosso P, Basile V, Martínez R, Métais E, Meziane F, (eds). Natural Language Processing and Information Systems, 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022 Proceedings; 2022 June 15-17; Valencia, Spain. Cham: Springer; 2022. p. 172-83.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101017255
• dc.rights © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Neural Transformers
• dc.subject.keyword Linguistic Features
• dc.subject.keyword Sign Gloss Machine Translation
• dc.subject.keyword Sign Language
• dc.title Linguistically enhanced text to sign gloss machine translation
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion