Transcription of Hierarchical Attention Networks for Document Classification
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Hierarchical Attention Networks for Document Classification
Proceedings of NAACL-HLT 2016, pages 1480 1489,San Diego, California, June 12-17, 2016 Association for Computational LinguisticsHierarchical Attention Networks for Document ClassificationZichao Yang1, Diyi Yang1, Chris Dyer1, Xiaodong He2, Alex Smola1, Eduard Hovy11 Carnegie Mellon University,2 Microsoft Research, Redmondfzichaoy, diyiy, cdyer, propose a Hierarchical Attention networkfor Document Classification . Our model hastwo distinctive characteristics: (i) it has a hier-archical structure that mirrors the hierarchicalstructure of documents; (ii) it has two levelsof Attention mechanisms applied at the word-and sentence-level, enabling it to attend dif-ferentially to more and less important con-tent when constructing the Document repre-sentation. Experiments conducted on six largescale text Classification tasks demonstrate thatthe proposed architecture outperform previousmethods by a substantial margin.
2 Hierarchical Attention Networks The overall architecture of the Hierarchical Atten-tion Network (HAN) is shown in Fig. 2. It con-sists of several parts: a word sequence encoder, a word-level attention layer, a sentence encoder and a sentence-level attention layer. We describe the de-tails of different components in the following sec-tions.
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