ViMRC - VLSP 2021: Improving Retrospective Reader for Vietnamese Machine Reading Comprehension

Abstract

In recent years, there are multiple systems (eg. search engines and dialogue systems) that require machines to be able to read and understand human text to serve several tasks in application. Machine Reading Comprehension (MRC) has posed a challenge to the Natural Language Processing (NLP) community in teaching machines to understand the meaning of human text in order to answer questions provided. Specifically in this challenge, the dataset contains questions that can be unanswerable, otherwise the answers can be extracted from the given passages. To deal with this challenge, our works mainly based on a recent approach, known as Retrospective Reader, to confronting unanswerable questions. Additionally, we focuses on enhancing the ability of answer extraction by applying properly attention mechanism and improving the representation ability through semantic information. Besides, we also present an ensemble way to acquire significant improvement in results provided by single models. Our method achieves 1$^{st}$ place on Vietnamese MRC shared task at the $8^{th}$ International Workshop on Vietnamese Language and Speech Processing (VLSP) with F1-score of \textbf{0.77241} and exact match (EM) of \textbf{0.66137} on the private test phase. For research purpose, our source code is available at \url{https://github.com/NamCyan/MRC\_VLSP2021}