Hien Nguyen


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VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering
Khang Le | Hien Nguyen | Tung Le Thanh | Minh Nguyen
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Vietnamese is the native language of over 98 million people in the world. However, existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. We introduce VIMQA, a new Vietnamese dataset with over 10,000 Wikipedia-based multi-hop question-answer pairs. The dataset is human-generated and has four main features: (1) The questions require advanced reasoning over multiple paragraphs. (2) Sentence-level supporting facts are provided, enabling the QA model to reason and explain the answer. (3) The dataset offers various types of reasoning to test the model’s ability to reason and extract relevant proof. (4) The dataset is in Vietnamese, a low-resource language. We also conduct experiments on our dataset using state-of-the-art Multilingual single-hop and multi-hop QA methods. The results suggest that our dataset is challenging for existing methods, and there is room for improvement in Vietnamese QA systems. In addition, we propose a general process for data creation and publish a framework for creating multilingual multi-hop QA datasets. The dataset and framework are publicly available to encourage further research in Vietnamese QA systems.


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Natural Language Generation for Nature Conservation: Automating Feedback to Help Volunteers Identify Bumblebee Species
Steven Blake | Advaith Siddharthan | Hien Nguyen | Nirwan Sharma | Anne-Marie Robinson | Elaine O’Mahony | Ben Darvill | Chris Mellish | René van der Wal
Proceedings of COLING 2012