Attention based recurrent neural networks have shown advantages in representing natural language sentences Rocktäschel et al., 2015; Tan et al., 2015) . Based on recurrent neural networks (RNN), external attention information was added to hidden representations to get an attentive sentence representation. Despite the improvement over nonattentive models, the attention mechanism under RNN is not well studied. In this work, we analyze the deficiency of traditional attention based RNN models quantitatively and qualitatively. Then we present three new RNN models that add attention information before RNN hidden representation, which shows advantage in representing sentence and achieves new stateof-art results in answer selection task.
To represent questions more clearly, Wang et al. REF use recurrent neural networks (RNN) with an attention mechanism to express the questions and present three new RNN models with attention mechanisms, which achieve good results on answer selection tasks.
Inner Attention based Recurrent Neural Networks for Answer Selection
ACL
Computer Science