Search Results for author: Frederico S. Felisberto

Found 1 papers, 1 papers with code

Pruning and Sparsemax Methods for Hierarchical Attention Networks

2 code implementations8 Apr 2020 João G. Ribeiro, Frederico S. Felisberto, Isabel C. Neto

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.

Document Classification General Classification +1

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