What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study

TL;DR

This paper introduces a new dataset for semantic textual relatedness, demonstrating its reliability and usefulness for evaluating sentence representations and NLP tasks, filling a gap left by previous focus on semantic similarity.

Contribution

The paper presents STR-2022, a large, manually annotated dataset for semantic relatedness, and explores factors influencing sentence relatedness and its applications in NLP.

Findings

Human annotations are highly reliable with a correlation of 0.84.

STR-2022 effectively evaluates sentence representation methods.

The dataset supports various downstream NLP tasks.

Abstract

The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. Additionally, automatically determining relatedness has many applications such as question answering and summarization. However, prior NLP work has largely focused on semantic similarity, a subset of relatedness, because of a lack of relatedness datasets. In this paper, we introduce a dataset for Semantic Textual Relatedness, STR-2022, that has 5,500 English sentence pairs manually annotated using a comparative annotation framework, resulting in fine-grained scores. We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0.84. We use the dataset to explore questions on what makes sentences semantically related. We also show the utility of STR-2022 for evaluating automatic methods of sentence representation and for various downstream NLP tasks. Our dataset, data statement, and annotation questionnaire can be found at: https://doi.org/10.5281/zenodo.7599667