Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets

TL;DR

This survey reviews recent progress in multi-choice Machine Reading Comprehension, analyzing datasets and methodologies, and categorizing approaches into fine-tuned and prompt-tuned methods to guide future research.

Contribution

It introduces a refined classification system for datasets and categorizes recent methodologies, providing a comprehensive overview of the current landscape in multi-choice MRC.

Findings

Analyzed 30 benchmark datasets with attribute-based classification.

Categorized methodologies into fine-tuned and prompt-tuned approaches.

Discussed challenges and future directions in multi-choice MRC.

Abstract

This paper provides a thorough examination of recent developments in the field of multi-choice Machine Reading Comprehension (MRC). Focused on benchmark datasets, methodologies, challenges, and future trajectories, our goal is to offer researchers a comprehensive overview of the current landscape in multi-choice MRC. The analysis delves into 30 existing cloze-style and multiple-choice MRC benchmark datasets, employing a refined classification method based on attributes such as corpus style, domain, complexity, context style, question style, and answer style. This classification system enhances our understanding of each dataset's diverse attributes and categorizes them based on their complexity. Furthermore, the paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods. Fine-tuned methods involve adapting pre-trained language models (PLMs) to a specific task through retraining on domain-specific datasets, while prompt-tuned methods use prompts to guide PLM response generation, presenting potential applications in zero-shot or few-shot learning scenarios. By contributing to ongoing discussions, inspiring future research directions, and fostering innovations, this paper aims to propel multi-choice MRC towards new frontiers of achievement.