A language's syntax defines its elements and their structure. We may speak of string, tree, and graph languages 鈥 to convey the nature of the elements' structure. One may distinguish two forms of syntax: concrete versus abstract syntax . The former is tailored towards processing (reading, ...
To mitigate the problem of losing important semantic information due to pruning strategies, the model introduces a dependency tree contrastive learning mechanism (DCL) as an alternative to traditional pruning approaches. Specifically, for a given text, DCL-HGCN first uses the ChatGPT model to ...
然而,Dep.Tree 结构的固有性质可能会引入噪声,例如子句之间的不相关关系,例如图 2 中“great”和“dreadful”之间的“conj”关系,这不利于捕获每个方面的情感感知上下文,即,上下文内。此外,Dep.Tree 结构仅揭示单词之间的关系,因此在大多数情况下无法对句子的复杂(例如,条件、协调或对抗)关系进行建模,因此无法捕获...
然而,Dep.Tree结构的固有性质可能会引入噪声,如跨从句的不相关关系,如图2中的“great”和“dreadful”之间的“conj”关系,这不鼓励捕捉每个方面的情感感知上下文,即上下文内(intra-context)。此外,Dep.Tree结构只揭示了词与词之间的关系,因此在大多数情况下无法建模复杂的句子关系(如条件关系、协调关系或对立关系),...
existing works Tree2Seq Chen et al. (2017) 15.9 40.8M 9.4 38.1M existing works SE-NMT Wu et al. (2018) 16.4 42.5M 9.7 39.1M Gated-GNN2S Beck et al. (2018) 16.7 41.2M 9.8 38.8M this work Bi-RNN (2 layers encoder) 15.5 62.3M 9.3 58.2M this work Bi-RNN + forward RGSE 16.0...
Then, the source code is converted into a tree structure based on the data-augmented abstract syntax tree, the different types of edges are added to build the code feature graph, which not only focus on syntactic features, but also extract the data flow a...
The present invention relates to a method for generating a deep learning model graph and an abstract syntax tree for an integrated compiler, wherein a processor uses the same library to create a propagation graph for deep learning model inference and a backpropagation graph for learning a deep ...
Graph attention networkDependency treeSyntactic informationAspect-level sentiment classification (ALSC) aims to predict the sentiment polarity of specific aspects in the input text. In recent years, given the advantages of graph neural networks (GNNs) in capturing structural information, an increasing ...
Existing syntax-aware GCN methods construct the adjacency matrix by referring to whether two words are connected in the dependency tree. But they fail to model the word dependency type, which reflects how the words are linked in dependency trees. They cannot distinguish the different contributions ...
We confirm that the "syntactic GCN" is the best-performing GCN layer, make empirical observations about Transformers and GCNs based on comparative results and dependency tree statistics, and draw parallels between the Transformer and GCN models in terms of their ability to learn relational structure....