A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics

TL;DR

This paper provides a detailed survey of recent datasets and evaluation metrics for multi-hop machine reading comprehension, highlighting challenges, advances, and open issues in the field.

Contribution

It offers a comprehensive review of 15 multi-hop MRC datasets and evaluation metrics from 2017 to 2022, summarizing recent progress and identifying open research challenges.

Findings

Reviewed 15 datasets from 2017 to 2022

Analyzed evaluation metrics specific to multi-hop MRC

Discussed open issues and future directions

Abstract

Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC evaluation metrics and datasets. In this regard, first, the multi-hop MRC problem definition will be presented, then the evaluation metrics based on their multi-hop aspect will be investigated. Also, 15 multi-hop datasets have been reviewed in detail from 2017 to 2022, and a comprehensive analysis has been prepared at the end. Finally, open issues in this field have been discussed.