Towards Improving Predictive AAC using Crowdsourced Dialogues and Partner Context

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.