# Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning

## TL;DR

This paper enhances machine reading comprehension by integrating external relational knowledge through multi-task learning, enabling better commonsense reasoning and improving performance on benchmark datasets.

## Contribution

It introduces a novel multi-task learning approach that incorporates relation-aware auxiliary tasks into a pre-trained language model for commonsense MRC.

## Key findings

- Achieves superior performance on SemEval-2018 Task 11
- Outperforms baselines on Cloze Story Test
- Demonstrates effectiveness of relation-aware auxiliary tasks

## Abstract

This paper focuses on how to take advantage of external relational knowledge to improve machine reading comprehension (MRC) with multi-task learning. Most of the traditional methods in MRC assume that the knowledge used to get the correct answer generally exists in the given documents. However, in real-world task, part of knowledge may not be mentioned and machines should be equipped with the ability to leverage external knowledge. In this paper, we integrate relational knowledge into MRC model for commonsense reasoning. Specifically, based on a pre-trained language model (LM). We design two auxiliary relation-aware tasks to predict if there exists any commonsense relation and what is the relation type between two words, in order to better model the interactions between document and candidate answer option. We conduct experiments on two multi-choice benchmark datasets: the SemEval-2018 Task 11 and the Cloze Story Test. The experimental results demonstrate the effectiveness of the proposed method, which achieves superior performance compared with the comparable baselines on both datasets.