Abstract
Icon-based communication systems are widely used in the field of Augmentative and Alternative Communication. Typically, icon-based systems have lagged behind word- and character-based systems in terms of predictive typing functionality, due to the challenges inherent to training icon-based language models. We propose a method for synthesizing training data for use in icon-based language models, and explore two different modeling strategies. We propose a method to generate language models for corpus-less symbol-set.- Anthology ID:
- W18-3404
- Volume:
- Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–32
- Language:
- URL:
- https://aclanthology.org/W18-3404
- DOI:
- 10.18653/v1/W18-3404
- Cite (ACL):
- Shiran Dudy and Steven Bedrick. 2018. Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 25–32, Melbourne. Association for Computational Linguistics.
- Cite (Informal):
- Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication (Dudy & Bedrick, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-3404.pdf