Question Answering with Hierarchical Attention Networks

We investigate hierarchical attention networks for the task of question answering. For this purpose, we propose two different approaches: in the first, a document vector representation is built hierarchically from word-to-sentence level which is then used to infer the right answer. In the second, pointer sum attention is utilized to directly infer an answer from the attention values of the word and sentence representations. We evaluate our approach on the Children's Book Test, a cloze-style question answering dataset, and analyze the generated attention distributions. Our results show that, although a hierarchical approach does not offer much improvement over a shallow baseline, it does indeed offer a large performance boost when combining word and sentence attention with pointer sum attention.