1 code implementation • 31 Jan 2024 • Xanh Ho, Anh Khoa Duong Nguyen, An Tuan Dao, Junfeng Jiang, Yuki Chida, Kaito Sugimoto, Huy Quoc To, Florian Boudin, Akiko Aizawa
The number of Language Models (LMs) dedicated to processing scientific text is on the rise.
1 code implementation • 25 Dec 2023 • Dong Pham, Xanh Ho, Quang-Thuy Ha, Akiko Aizawa
The complexity of this task is compounded by the necessity for domain-specific knowledge and the limited availability of annotated data.
2 code implementations • 12 Feb 2023 • Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa
To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets.
Multi-hop Question Answering Open-Ended Question Answering +1
1 code implementation • 11 Oct 2022 • Xanh Ho, Saku Sugawara, Akiko Aizawa
Other results reveal that our probing questions can help to improve the performance of the models (e. g., by +10. 3 F1) on the main QA task and our dataset can be used for data augmentation to improve the robustness of the models.
no code implementations • 5 Sep 2022 • Xanh Ho, Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa
The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years.
1 code implementation • COLING 2020 • Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa
The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model.