bigIR at CheckThat! 2020: Multilingual BERT for Ranking Arabic Tweets by Check-worthiness

Maram Hasanain; Tamer Elsayed

bigIR at CheckThat! 2020: Multilingual BERT for Ranking Arabic Tweets by Check-worthiness

Maram Hasanain
, Tamer Elsayed

Computer Science and Engineering Department

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper describes the third-year participation of our bigIR group at Qatar University in CheckThat! lab at CLEF. This year we participated only in Arabic Task 1 that focuses on detecting check-worthy tweets on a given topic. We submitted four runs using both traditional classification models and a pre-trained language model: multilingual BERT (mBERT). Official results showed that our run using mBERT was the best among all our submitted runs. Furthermore, bigIR team was ranked third among all eight teams participated in the lab, with our best run ranked 6th among 28 runs.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	2696
Publication status	Published - 2020
Externally published	Yes
Event	11th Conference and Labs of the Evaluation Forum, CLEF 2020 - virtual, Online, Greece Duration: 22 Sept 2020 → 25 Sept 2020

Cite this

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title = "bigIR at CheckThat! 2020: Multilingual BERT for Ranking Arabic Tweets by Check-worthiness",

abstract = "This paper describes the third-year participation of our bigIR group at Qatar University in CheckThat! lab at CLEF. This year we participated only in Arabic Task 1 that focuses on detecting check-worthy tweets on a given topic. We submitted four runs using both traditional classification models and a pre-trained language model: multilingual BERT (mBERT). Official results showed that our run using mBERT was the best among all our submitted runs. Furthermore, bigIR team was ranked third among all eight teams participated in the lab, with our best run ranked 6th among 28 runs.",

author = "Maram Hasanain and Tamer Elsayed",

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N2 - This paper describes the third-year participation of our bigIR group at Qatar University in CheckThat! lab at CLEF. This year we participated only in Arabic Task 1 that focuses on detecting check-worthy tweets on a given topic. We submitted four runs using both traditional classification models and a pre-trained language model: multilingual BERT (mBERT). Official results showed that our run using mBERT was the best among all our submitted runs. Furthermore, bigIR team was ranked third among all eight teams participated in the lab, with our best run ranked 6th among 28 runs.

AB - This paper describes the third-year participation of our bigIR group at Qatar University in CheckThat! lab at CLEF. This year we participated only in Arabic Task 1 that focuses on detecting check-worthy tweets on a given topic. We submitted four runs using both traditional classification models and a pre-trained language model: multilingual BERT (mBERT). Official results showed that our run using mBERT was the best among all our submitted runs. Furthermore, bigIR team was ranked third among all eight teams participated in the lab, with our best run ranked 6th among 28 runs.

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M3 - Conference article

AN - SCOPUS:85121799695

SN - 1613-0073

VL - 2696

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 22 September 2020 through 25 September 2020

ER -

bigIR at CheckThat! 2020: Multilingual BERT for Ranking Arabic Tweets by Check-worthiness

Abstract

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