SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts
This work addresses the challenge of understanding implicit language in instructional texts, which is incremental as it builds on existing shared tasks and datasets.
The paper tackled the problem of rating the plausibility of clarifications in instructional texts, with the best system achieving an accuracy of 68.9% and an additional evaluation showing 75.2% accuracy in identifying contexts with multiple plausible clarifications.
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%.