  Recently, end-to-end memory networks have shown promising results on Question
Answering task, which encode the past facts into an explicit memory and perform
reasoning ability by making multiple computational steps on the memory.
However, memory networks conduct the reasoning on sentence-level memory to
output coarse semantic vectors and do not further take any attention mechanism
to focus on words, which may lead to the model lose some detail information,
especially when the answers are rare or unknown words. In this paper, we
propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the
past facts into sentence-level memory and word-level memory respectively. Then,
(k)-max pooling is exploited following reasoning module on the sentence-level
memory to sample the (k) most relevant sentences to a question and feed these
sentences into attention mechanism on the word-level memory to focus the words
in the selected sentences. Finally, the prediction is jointly learned over the
outputs of the sentence-level reasoning module and the word-level attention
mechanism. The experimental results demonstrate that our approach successfully
conducts answer selection on unknown words and achieves a better performance
than memory networks.

Information Retrieval;Artificial Intelligence;Computation and Language