  In this paper, we consider the problem of machine reading task when the
questions are in the form of keywords, rather than natural language. In recent
years, researchers have achieved significant success on machine reading
comprehension tasks, such as SQuAD and TriviaQA. These datasets provide a
natural language question sentence and a pre-selected passage, and the goal is
to answer the question according to the passage. However, in the situation of
interacting with machines by means of text, people are more likely to raise a
query in form of several keywords rather than a complete sentence. The
keyword-based query comprehension is a new challenge, because small variations
to a question may completely change its semantical information, thus yield
different answers. In this paper, we propose a novel neural network system that
consists a Demand Optimization Model based on a passage-attention neural
machine translation and a Reader Model that can find the answer given the
optimized question. The Demand Optimization Model optimizes the original query
and output multiple reconstructed questions, then the Reader Model takes the
new questions as input and locate the answers from the passage. To make
predictions robust, an evaluation mechanism will score the reconstructed
questions so the final answer strike a good balance between the quality of both
the Demand Optimization Model and the Reader Model. Experimental results on
several datasets show that our framework significantly improves multiple strong
baselines on this challenging task.
