VLSP 2021 - VieCap4H Challenge: Automatic Image Caption Generation for Healthcare Domain in Vietnamese

Abstract

Machine reading comprehension (MRC) is a challenging Natural Language Processing (NLP) research fieldand wide real-world applications. The great progress of this field in recents is mainly due to the emergence offew datasets for machine reading comprehension tasks with large sizes and deep learning. For the Vietnameselanguage, some datasets, such as UIT-ViQuAD [1] and UIT-ViNewsQA [2], most recently, UIT-ViQuAD 2.0 [3] - adataset of the competitive VLSP 2021-MRC Shared Task 1 . MRC systems must not only answer questions whennecessary but also tactfully abstain from answering when no answer is available according to the given passage.In this paper, we proposed two types of joint models for answerability prediction and pure-MRC prediction with/without a dependency mechanism to learn the correlation between a start position and end position in pure-MRCoutput prediction. Besides, we use ensemble models and a verification strategy by voting the best answer from thetop K answers of different models. Our proposed approach is evaluated on the benchmark VLSP 2021-MRC SharedTask challenge dataset UIT-ViQuAD 2.0 [3] shows that our approach is significantly better than the baseline.