Bach Xuan Ngo


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Using Convolution Neural Network with BERT for Stance Detection in Vietnamese
Oanh Tran | Anh Cong Phung | Bach Xuan Ngo
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Stance detection is the task of automatically eliciting stance information towards a specific claim made by a primary author. While most studies have been done for high-resource languages, this work is dedicated to a low-resource language, namely Vietnamese. In this paper, we propose an architecture using transformers to detect stances in Vietnamese claims. This architecture exploits BERT to extract contextual word embeddings instead of using traditional word2vec models. Then, these embeddings are fed into CNN networks to extract local features to train the stance detection model. We performed extensive comparison experiments to show the effectiveness of the proposed method on a public dataset1 Experimental results show that this proposed model outperforms the previous methods by a large margin. It yielded an accuracy score of 75.57% averaged on four labels. This sets a new SOTA result for future research on this interesting problem in Vietnamese.


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A Listwise Approach to Coreference Resolution in Multiple Languages
Oanh Thi Tran | Bach Xuan Ngo | Minh Le Nguyen | Akira Shimazu
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation