Standard
Fact Check-Worthiness Detection with Contrastive Ranking. / Hansen, Casper; Hansen, Christian; Simonsen, Jakob Grue; Lioma, Christina.
Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings. ed. / Avi Arampatzis; Evangelos Kanoulas; Theodora Tsikrika; Stefanos Vrochidis; Hideo Joho; Christina Lioma; Carsten Eickhoff; Aurélie Névéol; Aurélie Névéol; Linda Cappellato; Nicola Ferro. Springer, 2020. p. 124-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12260 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Hansen, C, Hansen, C
, Simonsen, JG & Lioma, C 2020,
Fact Check-Worthiness Detection with Contrastive Ranking. in A Arampatzis, E Kanoulas, T Tsikrika, S Vrochidis, H Joho, C Lioma, C Eickhoff, A Névéol, A Névéol, L Cappellato & N Ferro (eds),
Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12260 LNCS, pp. 124-130, 11th Conference and Labs of the Evaluation Forum, CLEF 2020, Thessaloniki, Greece,
22/09/2020.
https://doi.org/10.1007/978-3-030-58219-7_11APA
Hansen, C., Hansen, C.
, Simonsen, J. G., & Lioma, C. (2020).
Fact Check-Worthiness Detection with Contrastive Ranking. In A. Arampatzis, E. Kanoulas, T. Tsikrika, S. Vrochidis, H. Joho, C. Lioma, C. Eickhoff, A. Névéol, A. Névéol, L. Cappellato, & N. Ferro (Eds.),
Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings (pp. 124-130). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12260 LNCS
https://doi.org/10.1007/978-3-030-58219-7_11Vancouver
Hansen C, Hansen C
, Simonsen JG, Lioma C.
Fact Check-Worthiness Detection with Contrastive Ranking. In Arampatzis A, Kanoulas E, Tsikrika T, Vrochidis S, Joho H, Lioma C, Eickhoff C, Névéol A, Névéol A, Cappellato L, Ferro N, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings. Springer. 2020. p. 124-130. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12260 LNCS).
https://doi.org/10.1007/978-3-030-58219-7_11Author
Hansen, Casper ; Hansen, Christian ; Simonsen, Jakob Grue ; Lioma, Christina. / Fact Check-Worthiness Detection with Contrastive Ranking. Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings. editor / Avi Arampatzis ; Evangelos Kanoulas ; Theodora Tsikrika ; Stefanos Vrochidis ; Hideo Joho ; Christina Lioma ; Carsten Eickhoff ; Aurélie Névéol ; Aurélie Névéol ; Linda Cappellato ; Nicola Ferro. Springer, 2020. pp. 124-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12260 LNCS).
Bibtex
@inproceedings{912b021041784e28bc2abdb0070dcf06,
title = "Fact Check-Worthiness Detection with Contrastive Ranking",
abstract = "Check-worthiness detection aims at predicting which sentences should be prioritized for fact-checking. A typical use is to rank sentences in political debates and speeches according to their degree of check-worthiness. We present the first direct optimization of sentence ranking for check-worthiness; in contrast, all previous work has solely used standard classification based loss functions. We present a recurrent neural network model that learns a sentence encoding, from which a check-worthiness score is predicted. The model is trained by jointly optimizing a binary cross entropy loss, as well as a ranking based pairwise hinge loss. We obtain sentence pairs for training through contrastive sampling, where for each sentence we find the top most semantically similar sentences with opposite label. Through a comparison to existing state-of-the-art check-worthiness methods, we find that our approach improves the MAP score by 11%.",
keywords = "Check-worthiness, Contrastive ranking, Neural networks",
author = "Casper Hansen and Christian Hansen and Simonsen, {Jakob Grue} and Christina Lioma",
year = "2020",
doi = "10.1007/978-3-030-58219-7_11",
language = "English",
isbn = "9783030582180",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "124--130",
editor = "Avi Arampatzis and Evangelos Kanoulas and Theodora Tsikrika and Stefanos Vrochidis and Hideo Joho and Christina Lioma and Carsten Eickhoff and Aur{\'e}lie N{\'e}v{\'e}ol and Aur{\'e}lie N{\'e}v{\'e}ol and Linda Cappellato and Nicola Ferro",
booktitle = "Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings",
address = "Switzerland",
note = "11th Conference and Labs of the Evaluation Forum, CLEF 2020 ; Conference date: 22-09-2020 Through 25-09-2020",
}
RIS
TY - GEN
T1 - Fact Check-Worthiness Detection with Contrastive Ranking
AU - Hansen, Casper
AU - Hansen, Christian
AU - Simonsen, Jakob Grue
AU - Lioma, Christina
PY - 2020
Y1 - 2020
N2 - Check-worthiness detection aims at predicting which sentences should be prioritized for fact-checking. A typical use is to rank sentences in political debates and speeches according to their degree of check-worthiness. We present the first direct optimization of sentence ranking for check-worthiness; in contrast, all previous work has solely used standard classification based loss functions. We present a recurrent neural network model that learns a sentence encoding, from which a check-worthiness score is predicted. The model is trained by jointly optimizing a binary cross entropy loss, as well as a ranking based pairwise hinge loss. We obtain sentence pairs for training through contrastive sampling, where for each sentence we find the top most semantically similar sentences with opposite label. Through a comparison to existing state-of-the-art check-worthiness methods, we find that our approach improves the MAP score by 11%.
AB - Check-worthiness detection aims at predicting which sentences should be prioritized for fact-checking. A typical use is to rank sentences in political debates and speeches according to their degree of check-worthiness. We present the first direct optimization of sentence ranking for check-worthiness; in contrast, all previous work has solely used standard classification based loss functions. We present a recurrent neural network model that learns a sentence encoding, from which a check-worthiness score is predicted. The model is trained by jointly optimizing a binary cross entropy loss, as well as a ranking based pairwise hinge loss. We obtain sentence pairs for training through contrastive sampling, where for each sentence we find the top most semantically similar sentences with opposite label. Through a comparison to existing state-of-the-art check-worthiness methods, we find that our approach improves the MAP score by 11%.
KW - Check-worthiness
KW - Contrastive ranking
KW - Neural networks
U2 - 10.1007/978-3-030-58219-7_11
DO - 10.1007/978-3-030-58219-7_11
M3 - Article in proceedings
AN - SCOPUS:85092139148
SN - 9783030582180
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 124
EP - 130
BT - Experimental IR Meets Multilinguality, Multimodality, and Interaction - 11th International Conference of the CLEF Association, CLEF 2020, Proceedings
A2 - Arampatzis, Avi
A2 - Kanoulas, Evangelos
A2 - Tsikrika, Theodora
A2 - Vrochidis, Stefanos
A2 - Joho, Hideo
A2 - Lioma, Christina
A2 - Eickhoff, Carsten
A2 - Névéol, Aurélie
A2 - Névéol, Aurélie
A2 - Cappellato, Linda
A2 - Ferro, Nicola
PB - Springer
T2 - 11th Conference and Labs of the Evaluation Forum, CLEF 2020
Y2 - 22 September 2020 through 25 September 2020
ER -
