循环神经网络RNN结构被广泛应用于自然语言处理、机器翻译、语音识别、文字识别等方向,本文主要介绍循环神经网络中的Sequence To Sequence模型,简单分析其原理和应用。 关键词循环神经网络;Seq2Seq;训练方法 Recurrent Neural Network (3) Sequence To Sequence Model Li Zhichao 18122618 School of Computer Engineering and...
Sequence to Sequence(seq2seq)与Attention、Transformer 最常见的Sequence to Sequence(序列到序列)的模型是机器翻译 在机器翻译任务中,有一个输入序列x1,x2,…,xm和一个输出序列y1,2,…,n,这两个序列的长度可以不同,机器翻译就是给定输入序列x1,x2,…,xm找到最可能的目标序列y1,2,…,n,即最大化给定x时y...
Policy Gradient算法缺点 使用该方法可以得到一个无偏的反馈,但是需要采样完整的序列才可以得到奖励,因此收敛速度可能非常慢,因此考虑时间差分的方法,即在每一步采样之后都能够得到一个反馈,可以考虑Actor-Critic方法和DQN等方法。 2.3 Actor-Critic Model 使用Actor-Critic的最大化目标是优势函数最大化,即: 使用AC方法...
对话摘要1: Multi-View Sequence-to-Sequence Models 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...
Paper:Sequence to Sequence Learning with Neural Networks 论文原文:PDF 论文被引:12780(2020/11/07) 论文年份:NIPS 2014 论文作者:Ilya Sutskever, Oriol Vinyals, Quoc V . Le. (Google) 文章目录 Abstract 1 Introduction 2 The model 3 Experiments 3.1 Dataset details 3.2 Decoding and Res......
但是其有一个弊端,即它需要有足够的标注数据,因此其并不适用于去做序列到序列的映射任务(map sequences to sequences)。本论文主要贡献在于提出了一种端到端(end-to-end)的神经网络模型,来学习这种映射关系。作者用一个多层的LSTM网络来将输入序列映射(编码)为一个固定大小纬度的向量,再用另外一个多层的LSTM...
Each of these models can use different RNN cells, but all of them accept encoder inputs and decoder inputs. This motivates the interfaces in the TensorFlow seq2seq library (tensorflow/tensorflow/python/ops/seq2seq.py). The basic RNN encoder-decoder sequence-to-sequence model works as follows...
Seq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a co...
In this paper, we propose a novel model incorporating a sequence-to-sequence model that consists two LSTMs, one encoder and one decoder. The encoder LSTM accepts input time series of arbitrary lengths, extracts information from the raw data and based on which the decoder LSTM constructs fixed ...
[1] Convolutional Sequence to Sequence Learning. https://arxiv.org/abs/1705.03122 「每周一起读」是由 PaperWeekly 发起的协同阅读小组。我们每周精选一篇优质好文,利用在线协同工具进行精读并发起讨论,在碎片化时代坚持深度阅读。目前已成立的专题小组有:Chatbot、机器翻译、知识图谱、GAN、推荐系统、QA和多模态。