Enhanced Chinese-Vietnamese Cross-Language Event Detection via Aligned Knowledge Event Graph

Chinese-Vietnamese cross-language event detection aims to cluster texts that describe the same events in Chinese and Vietnamese into corresponding event clusters. However, because Vietnamese is a low-resource language, directly using multilingual pre-trained models to align event representations in Chinese and Vietnamese texts yields suboptimal results, leading to poor performance in cross-lingual event detection. To address this challenge, we propose a method to enhance cross-lingual event detection between Chinese and Vietnamese by utilizing an aligned knowledge event graph. By leveraging aligned event knowledge, such as personal and place names, to establish correlations between events in different languages, we construct a cross-lingual aligned knowledge event graph. Under the constraint of relational associations, we use contrastive learning to model the similarities and differences between various events, making the representations of the same events in different languages more compact. This approach improves the model’s ability to represent Chinese-Vietnamese cross-lingual event texts and enhances the effectiveness of cross-lingual event detection. Experimental results demonstrate that our method, on multiple multilingual pre-trained models, achieves significant improvements across evaluation metrics such as normalized mutual information, adjusted normalized mutual information, and the adjusted rand coefficient.