然而,在对话过程中,不能限制人们主题交错和重新介入的这些方式,因此需要一种比堆栈更灵活的架构。 第二种:RNN。近年来,一种常见的选择是使用RNN来处理多轮对话序列。研究者认为,只要有足够的训练数据,RNN应该能够学习任何期望的行为。然而,在当前没有足够的语料库的情况下,并不能保证RNN可以学习生成这些行为。在现...
RNN (Henderson et al., 2014) 87.1 70.8 Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised gate MultiWOZ The MultiWOZ dataset is a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of 10k dialog...
简单来说,DialogueRNN主要使用了两种RNN编码上下文信息,第一种是全局RNN,用于编码整篇对话的上下文信息;第二种是说话人RNN,每个说话人RNN对应于一个说话人,且只编码该说话人的上下文信息。 DialogueRNN模型示意图 DialogueGCN 另一种编码上下文信息的方法是GCN,每个对话都是一个天然的图结构,不同时间、不同说话人的ut...
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0. machine-learningnatural-language-processingdeep-learningdialoguenatural...
受到《情绪认知理论(Cognitive Theory of Emotion)》(Schachter and Singer, 1962[13:1]; Scherer等人,2001[14:1])的启发,认知因素对情绪状态的形成具有潜在的决定作用。因此,在认知阶段,我们设计了多轮推理模块来迭代地提取和整合情感线索。推理模块架构如图2所示。
This paper presents some Recurrent Neural Network Language Generation (RNNLG) framework based models along with their analysis of how they extract intended meaning in terms of content planning (modelling semantic input) and surface realization (final sentence generation) on a proposed unaligned Hindi ...
(RNNs) and then compute a score between the last hidden state of two RNNs (Feng, Xiang, Glass, Wang, Zhou, 2015, Mueller, Thyagarajan, 2016, Lowe et al., 2015). The MV-LSTM (Wan et al., 2015) improved the design by deriving a similarity matrix between outputs of RNNs, and ...
pairing RNN-based NLG models with a proposed data counterfeiting method and a discriminative objective function, a pre-trained model can be quickly adapted to different domains with only a few examples. All of the findings presented are supported by both corpus-based and human evaluations....
Word-based RNN 这种模型有一下特点: • 直接使用ASR输出的结果,不用SLU(避免了SLU模块的误差),而且将DST当做是一个序列建模问题,直接使用RNN处理; • 传统判别式方法需要人工提取特征,而本文直接使用n-gram特征以重构那些人工特征; • 构造一个与slot无关的RNN组件,以解决判别式模型过拟合的现象,从而追踪未...
相比于之前一次性的交互,文章设计了一个interaction-block对每一个context中的句子都与response进行了多次交互(有点类似于MRC对于document和query进行反复重构的思路),并且在不同轮数、不同level的交互之上用RNN进行了轮数之间的抽象,更加强调对于context关系的(自动)推断。