In this work, we propose Attentive Pooling (AP), a two-way attention
mechanism for discriminative model training. In the context of pair-wise
ranking or classification with neural networks, AP enables the pooling layer to
be aware of the current input pair, in a way that information from the two
input items can directly influence the computation of each other's
representations. Along with such representations of the paired inputs, AP
jointly learns a similarity measure over projected segments (e.g. trigrams) of
the pair, and subsequently, derives the corresponding attention vector for each
input to guide the pooling. Our two-way attention mechanism is a general
framework independent of the underlying representation learning, and it has
been applied to both convolutional neural networks (CNNs) and recurrent neural
networks (RNNs) in our studies. The empirical results, from three very
different benchmark tasks of question answering/answer selection, demonstrate
that our proposed models outperform a variety of strong baselines and achieve
state-of-the-art performance in all the benchmarks.

我们提出了一种名为 Attentive Pooling 的方法，可用于在神经网络中进行排名或分类，并且能够学习一种类似度量，以便使得输入内容能够影响彼此的表示，并引导池化。我们在三个基准任务中得到了最新的实验结果，并优于各种强基线模型。