1. 基础模型 A. Sequence to sequence model:机器翻译、语音识别。(1. Sutskever et. al., 2014. Sequence to sequence learning with neural networks. 2. Cho et. al., 2014.
1. Basic Models Sequence to sequence(序列)模型在机器翻译和语音识别方面都有着广泛的应用。下面,我们来看一个机器翻译的简单例子: 针对该机器翻译问题,可以使用“编码网络(encoder network)”+“解码网络(decoder network)”两个RNN模型组合的形式来解决。encoder network将输入语句编码为一个特征向量,传递给decoder ...
To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The ...
5. 定向搜索的误差分析(Error analysis in beam search) 6. Bleu Score 7. Attention Model Intuition 8. Attention Models 9. Speech recognition 10. Trigger Word Detection 参考 回到顶部 1. 基础模型(Basic Model) Sequence to sequence模型(Seq2Seq) 从机器翻译到语音识别方面都有着广泛的应用。 举例: 该...
1. Basic Models Sequence to sequence(序列)模型在机器翻译和语音识别方面都有着广泛的应用。下面,我们来看一个机器翻译的简单例子: 针对该机器翻译问题,可以使用“编码网络(encoder network)”+“解码网络(decoder network)”两个RNN模型组合的形式来解决。encoder network将输入语句编码为一个特征向量,传递给decoder...
The evaluation process of Seq2seq PyTorch is to check the model output. Each pair of Sequence to sequence models will be feed into the model and generate the predicted words. After that you will look the highest value at each output to find the correct index. And in the end, you will ...
1. Basic Models Sequence to sequence(序列)模型在机器翻译和语音识别方面都有着广泛的应用。下面,我们来看一个机器翻译的简单例子: 针对该机器翻译问题,可以使用“编码网络(encoder network)”+“解码网络(decoder network)”两个RNN模型组合的形式来解决。encoder network将输入语句编码为一个特征向量,传递给decoder...
Paper:EMNLP 2020 - Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization Code:SALT-NLP/Multi-View-Seq2Seq conversation summarization 的难点: informal, verbose and repetitive, sprinkled with false-starts, back channeling, reconfifirmations, hesitations,...
In many applications of sequence-to-sequence models, the output of the decoder at time t is fed back and becomes the input of the decoder at time t+1. At test time, when decoding a sequence, this is how the sequence is constructed. During training, on the other hand, it is common ...
为什么要引入优势函数,这里主要是淡化state的影响,我们不再去关心state本身的好坏,而只关心在当前state下选取的action的好坏。优势函数计算如下: 优势函数刻画了当前状态下,每个动作对这个特定状态的好坏。V(s)是所有动作的奖励的期望值,因此优势函数大于0的动作所能获得的奖励高于期望值,而优势函数小于0的动作所能获得...