在语言生成领域中，“序列到序列”（sequence to sequence）的方法已被证明是一种强大的模型。它可以被应用于进行机器翻译（machine translation）、query改写（query rewriting）、图像描述（image captioning）等等。
本篇教程将会指导你通过训练一个“序列到序列”的神经网络机器翻译（NMT）模型来将法语翻译成英语。
我们遵循 [Neural Machine Translation by Jointly Learning to Align and Translate](http://arxiv.org/abs/1409.0473) 这篇文章，其中详细说明了模型架构，以及在WMT-14数据集上得到良好表现的训练过程。本篇教程在PaddlePaddle中重现了这一良好的训练结果。
在这个任务中，我们使用了一个编解码模型的扩展，它同时学习排列(align)与翻译。每当模型在翻译过程中生成了一个单词，它就会在源语句中搜索出最相关信息的位置的集合。解码器根据上下文向量预测出一个目标单词，这个向量与源中搜索出的位置和所有之前生成的目标单词有关。如想了解更多详细的解释，可以参考 [Neural Machine Translation by Jointly Learning to Align and Translate](http://arxiv.org/abs/1409.0473)。
我们在拥有50个节点的集群中训练模型，每个节点有两个6核CPU。我们在5天里训练了16个pass，其中每条pass花费了7个小时。model_dir中有16个子目录，每个里面都包含202MB的全部的模型参数。然后我们发现pass-00012的模型有着最高的BLEU值27.77（参考文献[BLEU: a Method for Automatic Evaluation of Machine Translation](http://www.aclweb.org/anthology/P02-1040.pdf)）。要下载解压这个模型，只需在linux下运行如下命令：
0 -11.1314 The <unk> <unk> about the width of the seats while large controls are at stake <e>
1 -11.1519 The <unk> <unk> on the width of the seats while large controls are at stake <e>
2 -11.5988 The <unk> <unk> about the width of the seats while large controls are at stake . <e>
1
0 -24.4149 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of the Dubai <unk> . <e>
1 -26.9524 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of Dubai ' s <unk> . <e>
2 -27.9574 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of Dubai ' s Dubai <unk> . <e>
...
- 这是集束搜索的结果，其中beam size是3
- 第一行的“0”和第6行的“1”表示生成数据的序列id
- 其他六行列出了集束搜索的结果
- 第二列是集束搜索的得分（从大到小）
- 第三列是生成的英语序列
- 有两个特殊标识：
-`<e>`：序列的结尾
-`<unk>`：不包含在字典中的单词
### BLEU评估 ###
对机器翻译的人工评估工作很广泛但也很昂贵。一篇论文 [BLEU: a Method for Automatic Evaluation of Machine Translation](http://www.aclweb.org/anthology/P02-1040.pdf) 展示了一种方法，当需要快速或者频繁的评估时，使用自动的替补来替代经验丰富的人工评判。[Moses](http://www.statmt.org/moses/) 是一个统计学的机器翻译系统，我们使用其中的 [multi-bleu.perl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) 来做BLEU评估。运行以下命令来下载这个脚本：
1.**Data Definiation**: We defines an SeqToSeq gen data in our example. It returns gen_conf as the configuration, following is its input arguments:
1.**Data Definiation**: We defines an SeqToSeq gen data in our example. It returns gen_conf as the configuration, following is its input arguments:
- data\_dir: directory of gen data
- data\_dir: directory of gen data
- is\_generating: whether this config is used for generating, here is false
- is\_generating: whether this config is used for generating, here is true
- gen\_result: file to store the generation result
- gen\_result: file to store the generation result
2.**Algorithm Configuration**: We use SGD traing algorithm in generation, and specify batch_size as 1 (each time generate one sequence), and learning rate as 0.
2.**Algorithm Configuration**: We use SGD traing algorithm in generation, and specify batch_size as 1 (each time generate one sequence), and learning rate as 0.
3.**Network Architecture**: Essentially the same as the training model.
3.**Network Architecture**: Essentially the same as the training model.
