@inproceedings{yim-etal-2022-stanford,
title = "{S}tanford {ML}ab at {S}em{E}val 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility",
author = "Yim, Thomas and
Lee, Junha and
Verma, Rishi and
Hickmann, Scott and
Zhu, Annie and
Sallade, Camron and
Ng, Ian and
Chi, Ryan and
Liu, Patrick",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.150/",
doi = "10.18653/v1/2022.semeval-1.150",
pages = "1067--1070",
abstract = "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. Given a set of steps in an instructional text, there are certain phrases that most plausibly fill that spot. We explored various possible architectures, including tree-based methods over GloVe embeddings, ensembled BERT and ELECTRA models, and GPT 2-based infilling methods."
}
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<abstract>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. Given a set of steps in an instructional text, there are certain phrases that most plausibly fill that spot. We explored various possible architectures, including tree-based methods over GloVe embeddings, ensembled BERT and ELECTRA models, and GPT 2-based infilling methods.</abstract>
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%0 Conference Proceedings
%T Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility
%A Yim, Thomas
%A Lee, Junha
%A Verma, Rishi
%A Hickmann, Scott
%A Zhu, Annie
%A Sallade, Camron
%A Ng, Ian
%A Chi, Ryan
%A Liu, Patrick
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F yim-etal-2022-stanford
%X 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. Given a set of steps in an instructional text, there are certain phrases that most plausibly fill that spot. We explored various possible architectures, including tree-based methods over GloVe embeddings, ensembled BERT and ELECTRA models, and GPT 2-based infilling methods.
%R 10.18653/v1/2022.semeval-1.150
%U https://aclanthology.org/2022.semeval-1.150/
%U https://doi.org/10.18653/v1/2022.semeval-1.150
%P 1067-1070
Markdown (Informal)
[Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility](https://aclanthology.org/2022.semeval-1.150/) (Yim et al., SemEval 2022)
ACL
- Thomas Yim, Junha Lee, Rishi Verma, Scott Hickmann, Annie Zhu, Camron Sallade, Ian Ng, Ryan Chi, and Patrick Liu. 2022. Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1067–1070, Seattle, United States. Association for Computational Linguistics.