  This paper presents a novel neural model - Dynamic Fusion Network (DFN), for
machine reading comprehension (MRC). DFNs differ from most state-of-the-art
models in their use of a dynamic multi-strategy attention process, in which
passages, questions and answer candidates are jointly fused into attention
vectors, along with a dynamic multi-step reasoning module for generating
answers. With the use of reinforcement learning, for each input sample that
consists of a question, a passage and a list of candidate answers, an instance
of DFN with a sample-specific network architecture can be dynamically
constructed by determining what attention strategy to apply and how many
reasoning steps to take. Experiments show that DFNs achieve the best result
reported on RACE, a challenging MRC dataset that contains real human reading
questions in a wide variety of types. A detailed empirical analysis also
demonstrates that DFNs can produce attention vectors that summarize information
from questions, passages and answer candidates more effectively than other
popular MRC models.
