Hierarchical Memory Networks for Answer Selection on Unknown Words

Understand

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.