@inproceedings{singh-2022-niksss-semeval,
title = "niksss at {S}em{E}val-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts",
author = "Singh, Nikhil",
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-1.154/",
doi = "10.18653/v1/2022.semeval-1.154",
pages = "1090--1093",
abstract = "This paper describes the 9th place system description for SemEval-2022 Task 7. The goal of this shared task was to develop computational models to predict how plausible a clarification made on an instructional text is. This shared task was divided into two Subtasks A and B. We attempted to solve these using various transformers-based architecture under different regime. We initially treated this as a text2text generation problem but comparing it with our recent approach we dropped it and treated this as a text-sequence classification and regression depending on the Subtask."
}
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<abstract>This paper describes the 9th place system description for SemEval-2022 Task 7. The goal of this shared task was to develop computational models to predict how plausible a clarification made on an instructional text is. This shared task was divided into two Subtasks A and B. We attempted to solve these using various transformers-based architecture under different regime. We initially treated this as a text2text generation problem but comparing it with our recent approach we dropped it and treated this as a text-sequence classification and regression depending on the Subtask.</abstract>
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%0 Conference Proceedings
%T niksss at SemEval-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts
%A Singh, Nikhil
%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 singh-2022-niksss-semeval
%X This paper describes the 9th place system description for SemEval-2022 Task 7. The goal of this shared task was to develop computational models to predict how plausible a clarification made on an instructional text is. This shared task was divided into two Subtasks A and B. We attempted to solve these using various transformers-based architecture under different regime. We initially treated this as a text2text generation problem but comparing it with our recent approach we dropped it and treated this as a text-sequence classification and regression depending on the Subtask.
%R 10.18653/v1/2022.semeval-1.154
%U https://aclanthology.org/2022.semeval-1.154/
%U https://doi.org/10.18653/v1/2022.semeval-1.154
%P 1090-1093
Markdown (Informal)
[niksss at SemEval-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts](https://aclanthology.org/2022.semeval-1.154/) (Singh, SemEval 2022)
ACL