  In conversational machine reading, systems need to interpret natural language
rules, answer high-level questions such as "May I qualify for VA health care
benefits?", and ask follow-up clarification questions whose answer is necessary
to answer the original question. However, existing works assume the rule text
is provided for each user question, which neglects the essential retrieval step
in real scenarios. In this work, we propose and investigate an open-retrieval
setting of conversational machine reading. In the open-retrieval setting, the
relevant rule texts are unknown so that a system needs to retrieve
question-relevant evidence from a collection of rule texts, and answer users'
high-level questions according to multiple retrieved rule texts in a
conversational manner. We propose MUDERN, a Multi-passage Discourse-aware
Entailment Reasoning Network which extracts conditions in the rule texts
through discourse segmentation, conducts multi-passage entailment reasoning to
answer user questions directly, or asks clarification follow-up questions to
inquiry more information. On our created OR-ShARC dataset, MUDERN achieves the
state-of-the-art performance, outperforming existing single-passage
conversational machine reading models as well as a new multi-passage
conversational machine reading baseline by a large margin. In addition, we
conduct in-depth analyses to provide new insights into this new setting and our
model.
