  Recognition errors are common in human communication. Similar errors often
lead to unwanted behaviour in dialogue systems or virtual assistants. In human
communication, we can recover from them by repeating misrecognized words or
phrases; however in human-machine communication this recovery mechanism is not
available. In this paper, we attempt to bridge this gap and present a system
that allows a user to correct speech recognition errors in a virtual assistant
by repeating misunderstood words. When a user repeats part of the phrase the
system rewrites the original query to incorporate the correction. This rewrite
allows the virtual assistant to understand the original query successfully. We
present an end-to-end 2-step attention pointer network that can generate the
the rewritten query by merging together the incorrectly understood utterance
with the correction follow-up. We evaluate the model on data collected for this
task and compare the proposed model to a rule-based baseline and a standard
pointer network. We show that rewriting the original query is an effective way
to handle repetition-based recovery and that the proposed model outperforms the
rule based baseline, reducing Word Error Rate by 19% relative at 2% False Alarm
Rate on annotated data.
