  In the era of loT (Internet of Things) we are surrounded by a plethora of Al
enabled devices that can transcribe images, video, audio, and sensors signals
into text descriptions. When such transcriptions are captured in activity
reports for monitoring, life logging and anomaly detection applications, a user
would typically request a summary or ask targeted questions about certain
sections of the report they are interested in. Depending on the context and the
type of question asked, a question answering (QA) system would need to
automatically determine whether the answer covers single-span or multi-span
text components. Currently available QA datasets primarily focus on single span
responses only (such as SQuAD[4]) or contain a low proportion of examples with
multiple span answers (such as DROP[3]). To investigate automatic selection of
single/multi-span answers in the use case described, we created a new smart
home environment dataset comprised of questions paired with single-span or
multi-span answers depending on the question and context queried. In addition,
we propose a RoBERTa[6]-based multiple span extraction question answering
(MSEQA) model returning the appropriate answer span for a given question. Our
experiments show that the proposed model outperforms state-of-the-art QA models
on our dataset while providing comparable performance on published individual
single/multi-span task datasets.
