## 4 Experiments and Discussion
### Experiment Setting  
XLM-R is fine-tuned with a learning rate of \(4\times 10^{-5}\) on each dataset and is then used to encode all nodes. A two-layer GAT with a hidden size of 512 and a batch size of 32 is employed in the experiments. For optimization, the AdamW optimizer [46] with a learning rate of \(3\times 10^{-5}\) is employed. The number of the most similar examples (\(K\)) in similarity edges is set to 3. The temperature coefficient of contrastive learning, \(\gamma\), is set to 0.1 and \(\lambda=1\), \(\mu=1\) in Equation (10). The maximum sequence lengths for intent classification and other classification tasks are set to 128 and 512, respectively, consistent with [18]. This study implements the XCLHG model with the Python (3.9.2) programming language under the support of the well-known PyTorch (1.10.0) machine-learning framework. All experimental evaluations are executed on a GPU (Geforce RTX 3090, 24 GB).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Category** & **Languages** & **Train** & **Test** \\ \hline \multirow{4}{*}{Amazon Review (Books)} & \multirow{2}{*}{2} & English & 2000 & 2000 \\  & & German & 2000 & 2000 \\  & & French & 2000 & 2000 \\  & & Japanese & 2000 & 2000 \\ \hline \multirow{4}{*}{Amazon Review (Dvd)} & \multirow{2}{*}{2} & English & 2000 & 2000 \\  & & German & 2000 & 2000 \\  & & French & 2000 & 2000 \\  & & Japanese & 2000 & 2000 \\ \hline \multirow{4}{*}{Amazon Review (Music)} & \multirow{2}{*}{2} & English & 2000 & 2000 \\  & & German & 2000 & 2000 \\  & & French & 2000 & 2000 \\  & & Japanese & 2000 & 2000 \\ \hline \multirow{4}{*}{XGLUE} & \multirow{4}{*}{10} & English & 10,000 & 10,000 \\  & & German & 10,000 & 10,000 \\ \cline{1-1}  & & French & 10,000 & 10,000 \\ \cline{1-1}  & & Spanish & 10,000 & 10,000 \\ \cline{1-1}  & & Russian & 10,000 & 10,000 \\ \hline \multirow{4}{*}{Multilingual SLU} & \multirow{4}{*}{12} & English & 30,521 & 8621 \\ \cline{1-1}  & & Spanish & 3617 & 3043 \\ \cline{1-1}  & & Thai & 2156 & 1692 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasets used to evaluate the proposed model.  
For part-of-speech tagging, the Stanford POS Tagger [47] is used for English text; SpaCy (SpaCy: [[https://spacy.io](https://spacy.io)]([https://spacy.io](https://spacy.io)) accessed on 12 May 2023) for German, French, and Spanish; MeCab (MeCab: [[https://taku910.github.io/mecab/](https://taku910.github.io/mecab/)]([https://taku910.github.io/mecab/](https://taku910.github.io/mecab/)) accessed on 12 May 2023) for Japanese; TLIK (TLIK: [[https://pypi.org/project/ttk/](https://pypi.org/project/ttk/)]([https://pypi.org/project/ttk/](https://pypi.org/project/ttk/)) accessed on 12 May 2023) for Thai; and NLTK (NLTK: [[https://www.nltk.org/](https://www.nltk.org/)]([https://www.nltk.org/](https://www.nltk.org/)) accessed on 12 May 2023) for Russian. For identifying dependency relations, the Stanford Dependency Parser is used for English text; SpaCy for German, French, and Spanish; CaboCha (CaboCha: [[http://chasen.org/](http://chasen.org/)]([http://chasen.org/](http://chasen.org/)) taku/software/cabocha/ accessed on 12 May 2023) for Japanese; and Stanza (Stanza: [[https://stanfordnlp.github.io/stanza/](https://stanfordnlp.github.io/stanza/)]([https://stanfordnlp.github.io/stanza/](https://stanfordnlp.github.io/stanza/)) accessed on 12 May 2023) for Thai and Russian. In data preprocessing, the Tencent Translation API (Tencent translation API: [[https://cloud.tencent.com/document/api/551/73920](https://cloud.tencent.com/document/api/551/73920)]([https://cloud.tencent.com/document/api/551/73920](https://cloud.tencent.com/document/api/551/73920)) accessed on 12 May 2023) is employed to translate both source and target language documents. The proposed XCLHG framework is compared with various baselines, including multilingual BERT, XLM, XLM-R, and mT5. Additionally, it is evaluated against state-of-the-art models such as CLHG [18], TG-CTC [28], Meta-X\({}_{NLG}\)[29], the Ensemble model [30], and LS-mBERT [26].