@inproceedings{liu-etal-2024-jn666,
title = "{JN}666 at {S}em{E}val-2024 Task 7: {N}um{E}val: Numeral-Aware Language Understanding and Generation",
author = "Liu, Xinyi and
Liu, Xintong and
Lu, Hengyang",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.76",
pages = "497--502",
abstract = "This paper is submitted for SemEval-2027 task 7: Enhancing the Model{'}s Understanding and Generation of Numerical Values. The dataset for this task is NQuAD, which requires us to select the most suitable option number from four numerical options to fill in the blank in a news article based on the context. Based on the BertForMultipleChoice model, we proposed two new models, MC BERT and SSC BERT, and improved the model{'}s numerical understanding ability by pre-training the model on numerical comparison tasks. Ultimately, our best-performing model achieved an accuracy rate of 79.40{\%}, which is 9.45{\%} higher than the accuracy rate of NEMo.",
}
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<abstract>This paper is submitted for SemEval-2027 task 7: Enhancing the Model’s Understanding and Generation of Numerical Values. The dataset for this task is NQuAD, which requires us to select the most suitable option number from four numerical options to fill in the blank in a news article based on the context. Based on the BertForMultipleChoice model, we proposed two new models, MC BERT and SSC BERT, and improved the model’s numerical understanding ability by pre-training the model on numerical comparison tasks. Ultimately, our best-performing model achieved an accuracy rate of 79.40%, which is 9.45% higher than the accuracy rate of NEMo.</abstract>
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%0 Conference Proceedings
%T JN666 at SemEval-2024 Task 7: NumEval: Numeral-Aware Language Understanding and Generation
%A Liu, Xinyi
%A Liu, Xintong
%A Lu, Hengyang
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F liu-etal-2024-jn666
%X This paper is submitted for SemEval-2027 task 7: Enhancing the Model’s Understanding and Generation of Numerical Values. The dataset for this task is NQuAD, which requires us to select the most suitable option number from four numerical options to fill in the blank in a news article based on the context. Based on the BertForMultipleChoice model, we proposed two new models, MC BERT and SSC BERT, and improved the model’s numerical understanding ability by pre-training the model on numerical comparison tasks. Ultimately, our best-performing model achieved an accuracy rate of 79.40%, which is 9.45% higher than the accuracy rate of NEMo.
%U https://aclanthology.org/2024.semeval-1.76
%P 497-502
Markdown (Informal)
[JN666 at SemEval-2024 Task 7: NumEval: Numeral-Aware Language Understanding and Generation](https://aclanthology.org/2024.semeval-1.76) (Liu et al., SemEval 2024)
ACL