Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication

Shiran Dudy, Steven Bedrick


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
Editors:
Reza Haffari, Colin Cherry, George Foster, Shahram Khadivi, Bahar Salehi
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-3404.pdf