To extend the proposed method for multilingual Graph-to-Text (G2T) generation tasks, several strategies can be implemented. First, leveraging multilingual Large Language Models (LLMs) that are pre-trained on diverse language corpora would be essential. These models, such as mBART or mT5, can generate text in multiple languages, allowing for the synthesis of graph-text pairs across different linguistic contexts.
Second, the source sentence collection process can be adapted to include multilingual Wikipedia articles, ensuring that the dataset encompasses a wide range of languages. This would involve implementing a rule-based algorithm similar to the one used for English, but tailored to accommodate the syntactic and semantic structures of various languages.
Additionally, the graph extraction process should be refined to account for language-specific nuances, such as idiomatic expressions and cultural references. This could involve training the LLM on language-specific datasets to enhance its understanding of context and meaning in different languages.
Finally, Data-QuestEval can be adapted to evaluate the consistency of graph-text pairs in multiple languages, ensuring that the generated text accurately reflects the information contained in the graph representations. By implementing these strategies, the proposed method can effectively support multilingual G2T generation tasks, broadening its applicability and impact.