@inproceedings{a189f93ddaa74b6fa5ac94026f1cbaf8,
title = "A Comparative Study of Ensemble Approaches to Fact-Checking for the FEVER Shared Task",
abstract = "The surge of information globally motivates for automated rumour detection. We use Fact-checking to detect rumours of the type misinformation. The FEVER-shared task is the Fact-checking task used for this comparative study. The task is divided into Document Retrieval, Sentence Selection, and Claim Verification components. We standardise TF-IDF for Document Retrieval, before creating the pipelines of Sentence Selection and Claim Verification algorithms. We evaluate each unique combination on the FEVER score, then compare the four pipelines to the baseline and state of the art. Our results show that the 2-way classification task using the Siamese BiLSTM achieves better Evidence Retrieval F1 scores than the state of the art models, and that the pipeline combinations, rival the state of the art for the Shared Task. The novelty of this research lies in the standardised text processing on novel pipeline combinations, allowing for comparable results, as well as the evaluation of the Siamese BiLSTM.",
keywords = "Claim Verification, Document Retrieval, Document selection, Evidence Retrieval, Fact-checking, FEVER",
author = "Oni, {Oluwabamigbe O.} and Zyl, {Terence L.Van}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 ; Conference date: 16-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
day = "16",
doi = "10.1109/CSDE50874.2020.9411564",
language = "English",
series = "2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020",
address = "United States",
}