## 3 The Proposed Method
### Heterogeneous Graph Attention Network  
For the construction of the heterogeneous graph, as shown on the right in Figure 2, this study employs a multi-layer GAT to aggregate information from nodes connected by each type of edge. Specifically, the GAT framework consists of three components: node encoding, graph structure representation, and a classifier. First, the node embeddings are generated using a multilingual pre-trained model. These embeddings are then fused using a dual-level graph attention mechanism, which first weighs the importance of different relation types at the edge-type level, and subsequently adjusts the contribution of each neighboring node at the node level. The integration of different edge types enables the model to comprehensively leverage complementary information. This integration process effectively captures heterogeneous graph information, resulting in robust and discriminative node embeddings. Specifically, the Graph Attention Network (GAT) individually encodes each type of edge (e.g., POS, dependency, translation, similarity), producing embeddings that encapsulate the diverse semantic and syntactic perspectives of the heterogeneous graph. Node Encoding  
Let \(x=(w_{1},w_{2},\ldots,w_{n})\) denotes an input document with n words. \(D=\{x_{1},x_{2},\ldots,x_{m}\}\) represents a dataset of source and target languages, where \(m\) is the number of documents. The encoder is a multilingual pre-trained language model, such as multilingual BERT [14; 39], XLM [9], XLM-RoBERTa [40], or mT5 [41], which can obtain rich contextual information. In this study, XLM-RoBERTa is specifically chosen to encode words and documents. \[h_{i}=f_{XLM-R}(x_{i}) \tag{2}\]  
\[z_{i}=f_{XLM-R}(w_{i}) \tag{3}\]  
where \(z_{i}\) and \(h_{i}\) denote the word and document node features, respectively. \(f_{XLM-R}\) is a model used to encode each node. Notably, the node features remain fixed during model training. Graph Representation Structure  
After encoding all nodes with the multilingual pretrained language model, the resulting features serve as input to the GAT, enabling the model to capture correlations between nodes more effectively. Inspired by [42], heterogeneous GATs exhibit variations in node importance, not only across different edge types but also among nodes connected by the same edge  
Figure 2: This figure illustrates the architecture of the proposed heterogeneous graph. For simplicity, only Noun, Verb, and ADJ edges are plotted for POS edges. type. Therefore, this study employs a GAT with a dual-level attention mechanism, comprising type-level attention and node-level attention to aggregate neighborhood information and generate node representations. The formulation of the type-level attention is defined as follows:  
\[\alpha_{\tau}=\frac{exp(LeakyReLU(a^{T}[W_{\tau_{i}}\|W_{\tau_{j}}]))}{\sum_{ \tau_{b}\in\tau}exp(LeakyReLU(a^{T}[W_{\tau_{i}}\|W_{\tau_{b}}]))} \tag{4}\]  
where \(W\) and \(a\) are the weight matrix and weight vector, respectively, \(LeakyReLU\) is the activation function, \(\|\) denotes the concatenation operator, and \(\tau\) represents the set of all neighboring nodes of different types. \(\alpha_{\tau}\) is the attention coefficient of node \(i\) and node \(j\) for type-level. Node importance varies across both different node types and among nodes of the same type. Here, the node-level attention can be defined as:  
\[\beta_{ij}=\frac{exp(LeakyReLU(b^{T}\alpha_{\tau}[W_{z_{i}}\|W_{z_{j}}]))}{ \sum_{z_{k}\in N_{i}}exp(LeakyReLU(b^{T}[W_{z_{i}}\|W_{z_{k}}]))} \tag{5}\]  
where \(N_{i}\) is the set of the neighboring nodes \(z_{k}\), \(\beta_{ij}\) is the node-level attention coefficient between neighboring node \(i\) and node \(j\), and \(b\) represents the weight vector.