原文地址: Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ 翻译这篇帖子一方面是为了记录自己的学习过程,强迫自己认真读帖,另一方面有关这篇帖子的翻译(包括作者推...
应该注意的是,也可以使用其他架构,例如 RNN 和去卷积神经网络的混合(Kalchbrenner and Blunsom,2013)。 3 从对齐模型到神经网络 在这一部分中,我们介绍了一种新的模型,该模型不是先对齐,然后再翻译,而是同时进行对齐和翻译。我们将此模型称为对齐和翻译模型(Alignment and Translation Model,ATM)。为了介绍这个模型...
The next RNN step takes the second input vector and hidden state #1 to create the output of that time step. Later in the post, we’ll use an animation like this to describe the vectors inside a neural machine translation model. 下一个time step中,RNN会获取第二个输入vector(input vecotr #...
This paper presents a neural machine translation model and dataset for the Chibchan language Bribri, with an average performance of BLEU 16.9卤1.7. This was trained on an extremely small dataset (5923 Bribri-Spanish pairs), providing evidence for the applicability of NMT in extremely low-...
https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ 论文题目:Neural Machine Translation by Jointly Learning to Align and Translate 论文地址:http://pdfs.semanticscholar.org/071b/16f25117fb6133480c6259227d54fc2a5ea0.pdf ...
Neural Machine Translation by Jointly Learning to Align and Translate 基于联合学习对齐和翻译的神经机器翻译 作者: Dzmitry Bahdanau1, KyungHyun Cho2, Yoshua Bengio*2 单位: 不来梅雅克布大学Jacobs University Bremen, Germany 蒙特利尔大学Universite de Montr ´ eal ...
(2016) presented an approach to train a bidirectional neural machine translation model, which introduced autoencoders on the monolingual corpora with source-to-target and target-to-source translation models as encoders and decoders by appending a reconstruction term to the training objective. He et...
model=[]Attention=[]withtf.variable_scope('decode'):dec_cell=tf.nn.rnn_cell.BasicRNNCell(n_hidden)dec_cell=tf.nn.rnn_cell.DropoutWrapper(dec_cell,output_keep_prob=0.5)inputs=tf.transpose(dec_inputs,[1,0,2])#decoder的输入hidden=enc_hidden#encoder每一层最后一个step的输出,将encoder的最后...
Fundamental Attentional Model Bahandanau Attentional Model 使用的是 global attention encoder使用了Bi-LSTM或者Bi-GRU 使用concat方式生成attention 解析顺序: ht−1 → at → ct → ht 参考论文Neural Machine Translation by Jointly Learning to Align and Translate ...
第一次把生成对抗学习的方法引入到机器翻译领域,提出了基于生成对抗训练(GAN)和深度强化学习(DRL)的新的机器翻译学习范式:adversarial neural Machine translation(ANMT),取得了比一些现有的NMT机器翻译模型更好的state-of-art成绩。 给出了详细的模型训练的设计,网络结构的设计,参数训练的细节,探究了学习速率learning ...