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.

本文提出并研究了对话式机器阅读的开放检索设置。在这种场景下，系统需要从一组规则文本中检索问题相关证据，并以对话方式回答用户的高级问题。我们提出了一个名为 MUDERN 的多段落语篇感知蕴涵推理网络，在我们创建的 OR-ShARC 数据集上实现了最先进的表现，超过了现有的单一段落和新的多段落对话式机器阅读模型。