@inproceedings{pouran-ben-veyseh-etal-2021-dpr,
title = "{DPR} at {S}em{E}val-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction",
author = "Pouran Ben Veyseh, Amir and
Dernoncourt, Franck and
Nguyen, Thien Huu",
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-1.47",
doi = "10.18653/v1/2021.semeval-1.47",
pages = "397--403",
abstract = "Scientific documents are replete with measurements mentioned in various formats and styles. As such, in a document with multiple quantities and measured entities, the task of associating each quantity to its corresponding measured entity is challenging. Thus, it is necessary to have a method to efficiently extract all measurements and attributes related to them. To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities, and conditions mentioned in a document. Our model employs a deep translation-based architecture to dynamically induce the important words in the document to classify the relation between a pair of entities. Furthermore, we introduce a novel regularization technique based on Information Bottleneck (IB) to filter out the noisy information from the induced set of important words. Our experiments on the recent SemEval 2021 Task 8 datasets reveal the effectiveness of the proposed model.",
}
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%0 Conference Proceedings
%T DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction
%A Pouran Ben Veyseh, Amir
%A Dernoncourt, Franck
%A Nguyen, Thien Huu
%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 pouran-ben-veyseh-etal-2021-dpr
%X Scientific documents are replete with measurements mentioned in various formats and styles. As such, in a document with multiple quantities and measured entities, the task of associating each quantity to its corresponding measured entity is challenging. Thus, it is necessary to have a method to efficiently extract all measurements and attributes related to them. To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities, and conditions mentioned in a document. Our model employs a deep translation-based architecture to dynamically induce the important words in the document to classify the relation between a pair of entities. Furthermore, we introduce a novel regularization technique based on Information Bottleneck (IB) to filter out the noisy information from the induced set of important words. Our experiments on the recent SemEval 2021 Task 8 datasets reveal the effectiveness of the proposed model.
%R 10.18653/v1/2021.semeval-1.47
%U https://aclanthology.org/2021.semeval-1.47
%U https://doi.org/10.18653/v1/2021.semeval-1.47
%P 397-403
Markdown (Informal)
[DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction](https://aclanthology.org/2021.semeval-1.47) (Pouran Ben Veyseh et al., SemEval 2021)
ACL