  We introduce a new task of rephrasing for a more natural virtual assistant.
Currently, virtual assistants work in the paradigm of intent slot tagging and
the slot values are directly passed as-is to the execution engine. However,
this setup fails in some scenarios such as messaging when the query given by
the user needs to be changed before repeating it or sending it to another user.
For example, for queries like 'ask my wife if she can pick up the kids' or
'remind me to take my pills', we need to rephrase the content to 'can you pick
up the kids' and 'take your pills' In this paper, we study the problem of
rephrasing with messaging as a use case and release a dataset of 3000 pairs of
original query and rephrased query. We show that BART, a pre-trained
transformers-based masked language model with auto-regressive decoding, is a
strong baseline for the task, and show improvements by adding a copy-pointer
and copy loss to it. We analyze different tradeoffs of BART-based and
LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the
best practical model.
