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Found 8 papers, 1 papers with code
UnImplicit Shared Task Report: Detecting Clarification Requirements in Instructional Text
This paper describes the data, task setup, and results of the shared task at the First Workshop on Understanding Implicit and Underspecified Language (UnImplicit).
Clarifying Implicit and Underspecified Phrases in Instructional Text
In this paper, we present a data set of such phrases in English from instructional texts together with multiple possible clarifications.
Towards Modeling Revision Requirements in wikiHow Instructions
wikiHow is a resource of how-to guidesthat describe the steps necessary to accomplish a goal.
Resolving Implicit References in Instructional Texts
The usage of (co-)referring expressions in discourse contributes to the coherence of a text.
SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts
We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts.
What Can We Learn from Noun Substitutions in Revision Histories?
The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy.
wikiHowToImprove: A Resource and Analyses on Edits in Instructional Texts
Instructional texts, such as articles in wikiHow, describe the actions necessary to accomplish a certain goal.
Team Kermit-the-frog at SemEval-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic Features
We achieved an accuracy of 0. 621 and a f1 score of 0. 694 in the competition, revealing the predictive power of negative sentiment for this task.
