Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. We focus in this paper on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description and speech recognition. All these systems are based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks, along with trained attention mechanisms. We report on experimental results with these systems, showing impressively good performance and the advantage of the attention mechanism.

本文介绍了使用深度神经网络中的递归神经网络和卷积神经网络等措施来实现注意力机制，以解决多元随机变量富含联合分布的结构化输出问题。通过典型任务如机器翻译、图像字幕生成、视频片段描述和语音识别实验，系统展示了很好的性能。

使用基于注意力机制的编码-解码网络描述多媒体内容