DialogueGCN首先使用双向LSTM编码全局信息,随后构建整篇对话的关系图,使用不同类型的边来表示时序以及说话人关系,据此使用relation GCN进一步编码上下文信息。 DialogueGCN模型示意图 总结一下,现在比较具有代表性的ERC模型,都是在编码全局上下文信息的基础上,辅助提供上下文信息;此外,现有的基于多模态的ERC模型还比较少。 三...
根据每条边两端的节点类型,将边进一步划分为6种类别:default-in-discourse, default-out-discourse, reverse-in-discourse, reverse-out-discourse, global, self 至此,整个Meeting Graph已经构造完成,接下来就是非常直接的embedding lookup+GCN编码+指针网络解码的pipeline,因为不重要也没有多少新意,所以这里不多赘述。
DialogGCN 58.10 - 64.18 - DialogCRN 58.39 - 66.20 - COSMIC 64.28 37.10 63.05 56.16 DAG-ERC 63.65 39.02 68.03 59.33 CoG-BART 64.81 (±0.19) 39.04 (±0.10) 66.18 (±0.45) 56.29 (±0.17)Table 4: The F1 scores for different values of α and β Metric Weighted Average F1 Datasets α=0.2...
为了在图上的对话状态节点之间传播信息,我们引入了图卷积网络 (GCN) 通过池化相邻节点的特征来更新具有结构感知功能的节点表示。 GCN 的输入包含一个节点嵌入矩阵 Undefined control sequence \R, 以及 邻接矩阵 Undefined control sequence \R 它表示对话状态图的结构。在对话状态图中,节点间的信息传播最多...
FOSTER Partners Ltd.URBAN healthTRANSPORTATION buildingsFAMILIESSKYSCRAPERSTECHNOLOGY assessmentADAPTIVE reuse of buildingsThe idea of the Special Series 'Future Scenarios' is to put the architectural project at the centre of debate as a complex phenomenon, able to build a synthesis of sci...
# only MTGCN and GatedGCN need to create the graph # zh or en ./run.sh graph <dataset> <zh/en> <cuda> 3. Check the information about the preprocessed dataset Show the length of the utterances, turns of the multi-turn setting and so on. ./run.sh stat <dataset> 4. Train N-gram...
In this paper, we propose a Relational Attention Enhanced Graph Convolutional Network (RAEGCN), which constructs the whole dialogue as a semantic interactive graph by emphasizing the speaker-related information and leveraging various inter-sentence dependencies. A dense connectivity mechanism is also ...
It was further proposed that these lysines could be crucial for gene expression regulation as they can be ubiquitinated (repressive effect) or acetylated by p300/CBP or GCN5 (General Control Of Amino Acid Synthesis Protein 5) which are enzymes involved in gene transcription [108]. In accordance...
Currently, the top three models in terms of accuracy for seven-class emotion recognition on AffectNet are POSTER++ (67.49%) [8], Emotion-GCN (66.46%) [9], and EmoAffectNet (66.37%) [10]. Other studies related to FER are as follows: Bakariya et al. [11] created a real-time system...
Specifically, a hierarchical graph convolutional network (HierGCN) is proposed to retain the object nodes and neighbour relationships locally, and then refines the object-object connections globally to obtain the final graph embeddings. A graph attention is further incorporated to dynamically attend to ...