  Conversational machine reading systems help users answer high-level questions
(e.g. determine if they qualify for particular government benefits) when they
do not know the exact rules by which the determination is made(e.g. whether
they need certain income levels or veteran status). The key challenge is that
these rules are only provided in the form of a procedural text (e.g. guidelines
from government website) which the system must read to figure out what to ask
the user. We present a new conversational machine reading model that jointly
extracts a set of decision rules from the procedural text while reasoning about
which are entailed by the conversational history and which still need to be
edited to create questions for the user. On the recently introduced ShARC
conversational machine reading dataset, our Entailment-driven Extract and Edit
network (E3) achieves a new state-of-the-art, outperforming existing systems as
well as a new BERT-based baseline. In addition, by explicitly highlighting
which information still needs to be gathered, E3 provides a more explainable
alternative to prior work. We release source code for our models and
experiments at https://github.com/vzhong/e3.
