  This paper introduces and evaluates two novel Hierarchical Attention Network
models [Yang et al., 2016] - i) Hierarchical Pruned Attention Networks, which
remove the irrelevant words and sentences from the classification process in
order to reduce potential noise in the document classification accuracy and ii)
Hierarchical Sparsemax Attention Networks, which replace the Softmax function
used in the attention mechanism with the Sparsemax [Martins and Astudillo,
2016], capable of better handling importance distributions where a lot of words
or sentences have very low probabilities. Our empirical evaluation on the IMDB
Review for sentiment analysis datasets shows both approaches to be able to
match the results obtained by the current state-of-the-art (without, however,
any significant benefits). All our source code is made available
athttps://github.com/jmribeiro/dsl-project.
