Search Results for author: Thomas Yim

Found 1 papers, 0 papers with code

Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility

no code implementations SemEval (NAACL) 2022 Thomas Yim, Junha Lee, Rishi Verma, Scott Hickmann, Annie Zhu, Camron Sallade, Ian Ng, Ryan Chi, Patrick Liu

In this paper, we detail the methods we used to determine the idiomaticity and plausibility of candidate words or phrases into an instructional text as part of the SemEval Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts.

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