Duy-Cat Can


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UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model
Duy-Cat Can | Quoc-An Nguyen | Quoc-Hung Duong | Minh-Quang Nguyen | Huy-Son Nguyen | Linh Nguyen Tran Ngoc | Quang-Thuy Ha | Mai-Vu Tran
Proceedings of the 20th Workshop on Biomedical Language Processing

This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop. We propose an extractive summarization architecture based on several scores and state-of-the-art techniques. We also present our novel prosper-thy-neighbour strategies to improve performance. Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner up by ROUGE-2 F1 score (over 13 participated teams).


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Overview of VLSP RelEx shared task: A Data Challenge for Semantic Relation Extraction from Vietnamese News
Vu Tran Mai | Hoang-Quynh Le | Duy-Cat Can | Thi Minh Huyen Nguyen | Tran Ngoc Linh Nguyen | Thanh Tam Doan
Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing


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A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction
Duy-Cat Can | Hoang-Quynh Le | Quang-Thuy Ha | Nigel Collier
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence. Each approach suffers from its own disadvantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel filters, namely RbSP (Richer-but-Smarter SDP). To exploit the representation behind the RbSP structure effectively, we develop a combined deep neural model with a LSTM network on word sequences and a CNN on RbSP. Experimental results on the SemEval-2010 dataset demonstrate improved performance over competitive baselines. The data and source code are available at https://github.com/catcd/RbSP.


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Large-scale Exploration of Neural Relation Classification Architectures
Hoang-Quynh Le | Duy-Cat Can | Sinh T. Vu | Thanh Hai Dang | Mohammad Taher Pilehvar | Nigel Collier
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our ‘Man for All Seasons’ approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.