Towards improving predictive AAC using crowdsourced dialogues and partner context

Document Type

Conference Proceeding

Publication Date



Department of Computer Science; Center for Human-Centered Computing

Publisher's Statement

Augmentative and Alternative Communication (AAC) devices typically rely on a language model to help make predictions or disambiguate user input. We investigate how to improve predictions in two-sided conversational dialogues. We collect and share a new corpus of crowdsourced everyday dialogues. We show how language models based on recurrent neural networks outperform N-gram models on these dialogues. We demonstrate further gains are possible using text obtained from an AAC user's communication partner, even when that text is partial or contains errors.

Publication Title

ASSETS '17 Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility