SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts

TL;DR

This paper presents SemEval-2022 Task 7, a shared challenge focused on automatically assessing the plausibility of clarifications in instructional texts, with multiple systems evaluated on a newly created dataset.

Contribution

It introduces a new dataset and task for plausibility assessment of clarifications, along with an evaluation of multiple systems' performance.

Findings

Best system achieved 68.9% accuracy in plausibility detection.

Top team predictions can identify multiple plausible clarifications with 75.2% accuracy.

The shared task fosters progress in understanding implicit and underspecified instructions.

Abstract

We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. The task of participating systems was to automatically determine the plausibility of a clarification in the respective context. In total, 21 participants took part in this task, with the best system achieving an accuracy of 68.9%. This report summarizes the results and findings from 8 teams and their system descriptions. Finally, we show in an additional evaluation that predictions by the top participating team make it possible to identify contexts with multiple plausible clarifications with an accuracy of 75.2%.