@inproceedings{tran-etal-2022-ijs,
title = "{IJS} at {T}ext{G}raphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection?",
author = "Tran, Thi Hong Hanh and
Martinc, Matej and
Doucet, Antoine and
Pollak, Senja",
editor = "Ustalov, Dmitry and
Gao, Yanjun and
Panchenko, Alexander and
Valentino, Marco and
Thayaparan, Mokanarangan and
Nguyen, Thien Huu and
Penn, Gerald and
Ramesh, Arti and
Jana, Abhik",
booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.textgraphs-1.12",
pages = "114--118",
abstract = "Natural Language Premise Selection (NLPS) is a mathematical Natural Language Processing (NLP) task that retrieves a set of applicable relevant premises to support the end-user finding the proof for a particular statement. In this research, we evaluate the impact of Transformer-based contextual information and different fundamental similarity scores toward NLPS. The results demonstrate that the contextual representation is better at capturing meaningful information despite not being pretrained in the mathematical background compared to the statistical approach (e.g., the TF-IDF) with a boost of around 3.00{\%} MAP@500.",
}
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<abstract>Natural Language Premise Selection (NLPS) is a mathematical Natural Language Processing (NLP) task that retrieves a set of applicable relevant premises to support the end-user finding the proof for a particular statement. In this research, we evaluate the impact of Transformer-based contextual information and different fundamental similarity scores toward NLPS. The results demonstrate that the contextual representation is better at capturing meaningful information despite not being pretrained in the mathematical background compared to the statistical approach (e.g., the TF-IDF) with a boost of around 3.00% MAP@500.</abstract>
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%0 Conference Proceedings
%T IJS at TextGraphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection?
%A Tran, Thi Hong Hanh
%A Martinc, Matej
%A Doucet, Antoine
%A Pollak, Senja
%Y Ustalov, Dmitry
%Y Gao, Yanjun
%Y Panchenko, Alexander
%Y Valentino, Marco
%Y Thayaparan, Mokanarangan
%Y Nguyen, Thien Huu
%Y Penn, Gerald
%Y Ramesh, Arti
%Y Jana, Abhik
%S Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F tran-etal-2022-ijs
%X Natural Language Premise Selection (NLPS) is a mathematical Natural Language Processing (NLP) task that retrieves a set of applicable relevant premises to support the end-user finding the proof for a particular statement. In this research, we evaluate the impact of Transformer-based contextual information and different fundamental similarity scores toward NLPS. The results demonstrate that the contextual representation is better at capturing meaningful information despite not being pretrained in the mathematical background compared to the statistical approach (e.g., the TF-IDF) with a boost of around 3.00% MAP@500.
%U https://aclanthology.org/2022.textgraphs-1.12
%P 114-118
Markdown (Informal)
[IJS at TextGraphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection?](https://aclanthology.org/2022.textgraphs-1.12) (Tran et al., TextGraphs 2022)
ACL