12 citations · 12 across the 6 of their papers we have counts for
6 papers
Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology
Son Quoc Tran, Matt Kretchmar
This paper proposes a novel training method to improve the robustness of Extractive Question Answering (EQA) models. Previous research has shown that existing models, when trained…
VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding
Phong Nguyen-Thuan Do, Son Quoc Tran, Phu Gia Hoang +2
The success of Natural Language Understanding (NLU) benchmarks in various languages, such as GLUE for English, CLUE for Chinese, KLUE for Korean, and IndoNLU for Indonesian, has fa…
AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions
Son Quoc Tran, Gia-Huy Do, Phong Nguyen-Thuan Do +2
The development of large high-quality datasets and high-performing models have led to significant advancements in the domain of Extractive Question Answering (EQA). This progress h…
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension
Son Quoc Tran, Phong Nguyen-Thuan Do, Kiet Van Nguyen +1
Although the curse of multilinguality significantly restricts the language abilities of multilingual models in monolingual settings, researchers now still have to rely on multiling…
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models
Son Quoc Tran, Phong Nguyen-Thuan Do, Uyen Le +1
Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. Nevertheless, their relative inability to defend against a…
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension
Kiet Van Nguyen, Son Quoc Tran, Luan Thanh Nguyen +3
One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual d…