Semantic Matching from Different Perspectives

TL;DR

This paper introduces a multi-perspective dataset for text similarity, highlighting the importance of evaluating similarity from various viewpoints, and analyzes existing models on this new benchmark.

Contribution

It releases the first multi-perspective text similarity dataset and evaluates popular models, providing insights and baselines for future research.

Findings

Multiple perspectives reveal nuanced similarity judgments.

Existing models show varying performance across perspectives.

Baseline results establish a foundation for future work.

Abstract

In this paper, we pay attention to the issue which is usually overlooked, i.e., \textit{similarity should be determined from different perspectives}. To explore this issue, we release a Multi-Perspective Text Similarity (MPTS) dataset, in which sentence similarities are labeled from twelve perspectives. Furthermore, we conduct a series of experimental analysis on this task by retrofitting some famous text matching models. Finally, we obtain several conclusions and baseline models, laying the foundation for the following investigation of this issue. The dataset and code are publicly available at Github\footnote{\url{https://github.com/autoliuweijie/MPTS}