Abstract
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-theart, 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