Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

Ioannis Bekoulis; Christina Papagiannopoulou; Nikos Deligiannis

Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

Ioannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis

Electronics and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

5 Citations (Scopus)

Abstract

Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.

Original language	English
Title of host publication	Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Editors	Anna Feldman, Giovanni Da San Martino, Chris Leberknight, Preslav Nakov
Publisher	Association for Computational Linguistics
Pages	23-28
Number of pages	6
ISBN (Electronic)	9781954085268
Publication status	Published - Jun 2021
Event	2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Online Duration: 6 Jun 2021 → 11 Jun 2021 Conference number: 2021 https://2021.naacl.org/

Publication series

Name	NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop

Conference

Conference	2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Abbreviated title	NAACL
Period	6/06/21 → 11/06/21
Internet address	https://2021.naacl.org/

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics.

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

Access to Document

https://www.aclweb.org/anthology/2021.nlp4if-1.4.pdf

Cite this

Bekoulis, I., Papagiannopoulou, C., & Deligiannis, N. (2021). Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking. In A. Feldman, G. Da San Martino, C. Leberknight, & P. Nakov (Eds.), Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda (pp. 23-28). (NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop). Association for Computational Linguistics. https://www.aclweb.org/anthology/2021.nlp4if-1.4.pdf

Bekoulis, Ioannis ; Papagiannopoulou, Christina ; Deligiannis, Nikos. / Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking. Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda. editor / Anna Feldman ; Giovanni Da San Martino ; Chris Leberknight ; Preslav Nakov. Association for Computational Linguistics, 2021. pp. 23-28 (NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop).

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title = "Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking",

abstract = "Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.",

author = "Ioannis Bekoulis and Christina Papagiannopoulou and Nikos Deligiannis",

note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.; 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL ; Conference date: 06-06-2021 Through 11-06-2021",

year = "2021",

month = jun,

language = "English",

series = "NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop",

publisher = "Association for Computational Linguistics",

pages = "23--28",

editor = "Anna Feldman and {Da San Martino}, Giovanni and Chris Leberknight and Preslav Nakov",

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Bekoulis, I, Papagiannopoulou, C & Deligiannis, N 2021, Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking. in A Feldman, G Da San Martino, C Leberknight & P Nakov (eds), Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda. NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop, Association for Computational Linguistics, pp. 23-28, 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 6/06/21. <https://www.aclweb.org/anthology/2021.nlp4if-1.4.pdf>

Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking. / Bekoulis, Ioannis; Papagiannopoulou, Christina; Deligiannis, Nikos.

Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda. ed. / Anna Feldman; Giovanni Da San Martino; Chris Leberknight; Preslav Nakov. Association for Computational Linguistics, 2021. p. 23-28 (NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

TY - GEN

T1 - Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

AU - Bekoulis, Ioannis

AU - Papagiannopoulou, Christina

AU - Deligiannis, Nikos

N1 - Conference code: 2021

PY - 2021/6

Y1 - 2021/6

N2 - Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.

AB - Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.

UR - http://www.scopus.com/inward/record.url?scp=85116296515&partnerID=8YFLogxK

M3 - Conference paper

T3 - NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop

SP - 23

EP - 28

BT - Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

A2 - Feldman, Anna

A2 - Da San Martino, Giovanni

A2 - Leberknight, Chris

A2 - Nakov, Preslav

PB - Association for Computational Linguistics

T2 - 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Y2 - 6 June 2021 through 11 June 2021

ER -

Bekoulis I, Papagiannopoulou C, Deligiannis N. Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking. In Feldman A, Da San Martino G, Leberknight C, Nakov P, editors, Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda. Association for Computational Linguistics. 2021. p. 23-28. (NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop).

Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

SRP11: Strategic Research Programme: Processing of large scale multi-dimensional, multi-spectral, multi-sensorial and distributed data (M³D²)

Cite this

Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Projects

SRP11: Strategic Research Programme: Processing of large scale multi-dimensional, multi-spectral, multi-sensorial and distributed data (M³D²)

Cite this