  Neural network models recently proposed for question answering (QA) primarily
focus on capturing the passage-question relation. However, they have minimal
capability to link relevant facts distributed across multiple sentences which
is crucial in achieving deeper understanding, such as performing multi-sentence
reasoning, co-reference resolution, etc. They also do not explicitly focus on
the question and answer type which often plays a critical role in QA. In this
paper, we propose a novel end-to-end question-focused multi-factor attention
network for answer extraction. Multi-factor attentive encoding using
tensor-based transformation aggregates meaningful facts even when they are
located in multiple sentences. To implicitly infer the answer type, we also
propose a max-attentional question aggregation mechanism to encode a question
vector based on the important words in a question. During prediction, we
incorporate sequence-level encoding of the first wh-word and its immediately
following word as an additional source of question type information. Our
proposed model achieves significant improvements over the best prior
state-of-the-art results on three large-scale challenging QA datasets, namely
NewsQA, TriviaQA, and SearchQA.
