  Healthcare question answering assistance aims to provide customer healthcare
information, which widely appears in both Web and mobile Internet. The
questions usually require the assistance to have proficient healthcare
background knowledge as well as the reasoning ability on the knowledge.
Recently a challenge involving complex healthcare reasoning, HeadQA dataset,
has been proposed, which contains multiple-choice questions authorized for the
public healthcare specialization exam. Unlike most other QA tasks that focus on
linguistic understanding, HeadQA requires deeper reasoning involving not only
knowledge extraction, but also complex reasoning with healthcare knowledge.
These questions are the most challenging for current QA systems, and the
current performance of the state-of-the-art method is slightly better than a
random guess. In order to solve this challenging task, we present a Multi-step
reasoning with Knowledge extraction framework (MurKe). The proposed framework
first extracts the healthcare knowledge as supporting documents from the large
corpus. In order to find the reasoning chain and choose the correct answer,
MurKe iterates between selecting the supporting documents, reformulating the
query representation using the supporting documents and getting entailment
score for each choice using the entailment model. The reformulation module
leverages selected documents for missing evidence, which maintains
interpretability. Moreover, we are striving to make full use of off-the-shelf
pre-trained models. With less trainable weight, the pre-trained model can
easily adapt to healthcare tasks with limited training samples. From the
experimental results and ablation study, our system is able to outperform
several strong baselines on the HeadQA dataset.
