Abstract
Natural language processing and the machine translation of spoken language (speech/text) has benefitted from significant scientific research and development in re-cent times, rapidly advancing the field. On the other hand, computational processing and modelling of signed language has unfortunately not garnered nearly as much interest, with sign languages generally being excluded from modern language technologies. Many deaf and hard-of-hearing individuals use sign language on a daily basis as their first language. For the estimated 72 million deaf people in the world, the exclusion of sign languages from modern natural language processing and machine translation technology, aggravates further the communication barrier that already exists for deaf and hard-of-hearing individuals. This research leverages a linguistically informed approach to the processing and modelling of signed language. We outline current challenges for sign language machine translation from both a linguistic and a technical prespective. We provide an account of our work in progress in the development of sign language lexicon entries and sign language lexeme repository entries for SLMT. We leverage Role and Reference Grammar together with the Sign_A computational framework with-in this development. We provide an XML description for Sign_A, which is utilised to document SL lexicon entries together with SL lexeme repository entries. This XML description is also leveraged in the development of an extension to Bahavioural Markup Language, which will be used within this development to link the divide be-tween the sign language lexicon and the avatar animation interface.- Anthology ID:
- 2022.amta-research.18
- Volume:
- Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
- Month:
- September
- Year:
- 2022
- Address:
- Orlando, USA
- Editors:
- Kevin Duh, Francisco Guzmán
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 240–251
- Language:
- URL:
- https://aclanthology.org/2022.amta-research.18
- DOI:
- Cite (ACL):
- Irene Murtagh, Víctor Ubieto Nogales, and Josep Blat. 2022. Sign Language Machine Translation and the Sign Language Lexicon: A Linguistically Informed Approach. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 240–251, Orlando, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Sign Language Machine Translation and the Sign Language Lexicon: A Linguistically Informed Approach (Murtagh et al., AMTA 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.amta-research.18.pdf