  The rise of personal assistants has made conversational question answering
(ConvQA) a very popular mechanism for user-system interaction. State-of-the-art
methods for ConvQA over knowledge graphs (KGs) can only learn from crisp
question-answer pairs found in popular benchmarks. In reality, however, such
training data is hard to come by: users would rarely mark answers explicitly as
correct or wrong. In this work, we take a step towards a more natural learning
paradigm - from noisy and implicit feedback via question reformulations. A
reformulation is likely to be triggered by an incorrect system response,
whereas a new follow-up question could be a positive signal on the previous
turn's answer. We present a reinforcement learning model, termed CONQUER, that
can learn from a conversational stream of questions and reformulations. CONQUER
models the answering process as multiple agents walking in parallel on the KG,
where the walks are determined by actions sampled using a policy network. This
policy network takes the question along with the conversational context as
inputs and is trained via noisy rewards obtained from the reformulation
likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark
with about 11k natural conversations containing around 205k reformulations.
Experiments show that CONQUER successfully learns to answer conversational
questions from noisy reward signals, significantly improving over a
state-of-the-art baseline.
