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

1st place in the Machine Reading Comprehension at VLSP 2021

This repository contains code for the vc-tus team in the VLSP2021 competition of Machine Reading Comprehension Shared Task. Our team achieves the 1st rank in the competition. More detail about task description, dataset, and ranking scores is provided in the organizer's report VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension.

Table of content

• Contributions
• Methodology
• Experimental results
• Leader board
• Citation
• Contact us

Contributions

1. This work introduces a more appropriate way to apply attention mechanism variants and utilize layer aggregation techniques to improve Retro-Reader and apply it to the Vietnamese MRC problem.

2. We present an ensemble approach to achieve outstanding results.

3. Experimental results show that our methods can yield substantial improvements to Retro-Reader and attain the 1st rank in VLSP2021-MRC Shared Task.

Methodology

We use two modules in our MRC system.

• A classifier module is used to determine whether a question is answerable when giving a passage, called Answerability Classification Module (ACM).

• Answer Extraction Module (AEM) aims to extract the answer from the passage.

Experimental results

Leader board

• Public test # Private test

Rank	Team	F1	EM	#	Rank	Team	F1	EM
	Human	87.335	81.818	#		Human	82.849	75.500
1	NLP_HUST	84.236	77.728	#	1	Our team	77.241	66.137
2	NTQ	84.089	77.990	#	2	ebisu_uit	77.222	67.430
3	ebisu_uit	82.622	73.698	#	3	F-NLP	76.456	64.655
4	Our team	81.013	71.316	#

Citation

More details can be found in our paper.

If you're using our work in your research or applications, please cite using this BibTeX:

@article{nam2022vimrc,
  title={ViMRC-VLSP 2021: Improving Retrospective Reader for Vietnamese Machine Reading Comprehension},
  author={Nam, Le Hai and Duc, Nguyen Sy and Quan, Chu Quoc and Van Vi, Ngo},
  journal={VNU Journal of Science: Computer Science and Communication Engineering},
  volume={38},
  number={2},
  year={2022}
}

Contact us

If you have any questions, comments or suggestions, please do not hesitate to contact us via nam.lh173264@gmail.com