Nguyen Vo

Also published as: Nguyen Ha Vo


Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
Nguyen Vo | Kyumin Lee
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection methods focused on either only word-level attention or evidence-level attention, which may result in suboptimal performance. In this paper, we propose a Hierarchical Multi-head Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multi-head document-level attention, which aid explanation in both word-level and evidence-level. Experiments on two real-word datasets show that our model outperforms seven state-of-the-art baselines. Improvements over baselines are from 6% to 18%. Our source code and datasets are released at


Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
Nguyen Vo | Kyumin Lee
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users’ consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters’ followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at


Identifying Various Kinds of Event Mentions in K-Parser Output
Arpit Sharma | Nguyen Vo | Somak Aditya | Chitta Baral
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

The NL2KR Platform for building Natural Language Translation Systems
Nguyen Vo | Arindam Mitra | Chitta Baral
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recognizing Social Constructs from Textual Conversation
Somak Aditya | Chitta Baral | Nguyen Ha Vo | Joohyung Lee | Jieping Ye | Zaw Naung | Barry Lumpkin | Jenny Hastings | Richard Scherl | Dawn M. Sweet | Daniela Inclezan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies