@inproceedings{shukla-etal-2021-noobs,
title = "Noobs at {S}emeval-2021 Task 4: Masked Language Modeling for abstract answer prediction",
author = "Shukla, Shikhar and
Sarthak, Sarthak and
Arya, Karm Veer",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.107",
doi = "10.18653/v1/2021.semeval-1.107",
pages = "805--809",
abstract = "This paper presents the system developed by our team for Semeval 2021 Task 4: Reading Comprehension of Abstract Meaning. The aim of the task was to benchmark the NLP techniques in understanding the abstract concepts present in a passage, and then predict the missing word in a human written summary of the passage. We trained a Roberta-Large model trained with a masked language modeling objective. In cases where this model failed to predict one of the available options, another Roberta-Large model trained as a binary classifier was used to predict correct and incorrect options. We used passage summary generated by Pegasus model and question as inputs. Our best solution was an ensemble of these 2 systems. We achieved an accuracy of 86.22{\%} on subtask 1 and 87.10{\%} on subtask 2.",
}
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<abstract>This paper presents the system developed by our team for Semeval 2021 Task 4: Reading Comprehension of Abstract Meaning. The aim of the task was to benchmark the NLP techniques in understanding the abstract concepts present in a passage, and then predict the missing word in a human written summary of the passage. We trained a Roberta-Large model trained with a masked language modeling objective. In cases where this model failed to predict one of the available options, another Roberta-Large model trained as a binary classifier was used to predict correct and incorrect options. We used passage summary generated by Pegasus model and question as inputs. Our best solution was an ensemble of these 2 systems. We achieved an accuracy of 86.22% on subtask 1 and 87.10% on subtask 2.</abstract>
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%0 Conference Proceedings
%T Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer prediction
%A Shukla, Shikhar
%A Sarthak, Sarthak
%A Arya, Karm Veer
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shukla-etal-2021-noobs
%X This paper presents the system developed by our team for Semeval 2021 Task 4: Reading Comprehension of Abstract Meaning. The aim of the task was to benchmark the NLP techniques in understanding the abstract concepts present in a passage, and then predict the missing word in a human written summary of the passage. We trained a Roberta-Large model trained with a masked language modeling objective. In cases where this model failed to predict one of the available options, another Roberta-Large model trained as a binary classifier was used to predict correct and incorrect options. We used passage summary generated by Pegasus model and question as inputs. Our best solution was an ensemble of these 2 systems. We achieved an accuracy of 86.22% on subtask 1 and 87.10% on subtask 2.
%R 10.18653/v1/2021.semeval-1.107
%U https://aclanthology.org/2021.semeval-1.107
%U https://doi.org/10.18653/v1/2021.semeval-1.107
%P 805-809
Markdown (Informal)
[Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer prediction](https://aclanthology.org/2021.semeval-1.107) (Shukla et al., SemEval 2021)
ACL