Understanding Disrupted Sentences Using Underspecified Abstract Meaning Representation

Voice assistant accessibility is generally overlooked as today's spoken dialogue systems are trained on huge corpora to help them understand the 'average' user. This raises frustrating barriers for certain user groups as their speech shifts from the average. People with dementia pause more frequently mid-sentence for example, and people with hearing impairments may mispronounce words learned post-diagnosis. We explore whether semantic parsing can improve accessibility for people with nonstandard speech, and consequently become more robust to external disruptions like dogs barking, sirens passing, or doors slamming mid-utterance. We generate corpora of disrupted sentences paired with their underspecified Abstract Meaning Representation (AMR) graphs, and use these to train pipelines to understand and repair disruptions. Our best disruption recovery pipeline lost only 1.6% graph similarity f-score when compared to a model given the full original sentence.