  Conversational question answering (ConvQA) over law knowledge bases (KBs)
involves answering multi-turn natural language questions about law and hope to
find answers in the law knowledge base. Despite many methods have been
proposed. Existing law knowledge base ConvQA model assume that the input
question is clear and can perfectly reflect user's intention. However, in real
world, the input questions are noisy and inexplict. This makes the model hard
to find the correct answer in the law knowledge bases. In this paper, we try to
use reinforcement learning to solve this problem. The reinforcement learning
agent can automatically learn how to find the answer based on the input
question and the conversation history, even when the input question is
inexplicit. We test the proposed method on several real world datasets and the
results show the effectivenss of the proposed model.
