Standard
It takes nine to smell a rat : Neural multi-task learning for check-worthiness prediction. / Vasileva, Slavena; Atanasova, Pepa; Màrquez, Lluís; Barrón-Cedeño, Alberto; Nakov, Preslav.
International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings. ed. / Galia Angelova; Ruslan Mitkov; Ivelina Nikolova; Irina Temnikova; Irina Temnikova. Incoma Ltd, 2019. p. 1229-1239 (International Conference Recent Advances in Natural Language Processing, RANLP, Vol. 2019-September).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Vasileva, S
, Atanasova, P, Màrquez, L, Barrón-Cedeño, A & Nakov, P 2019,
It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction. in G Angelova, R Mitkov, I Nikolova, I Temnikova & I Temnikova (eds),
International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings. Incoma Ltd, International Conference Recent Advances in Natural Language Processing, RANLP, vol. 2019-September, pp. 1229-1239, 12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019, Varna, Bulgaria,
02/09/2019.
https://doi.org/10.26615/978-954-452-056-4_141
APA
Vasileva, S.
, Atanasova, P., Màrquez, L., Barrón-Cedeño, A., & Nakov, P. (2019).
It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction. In G. Angelova, R. Mitkov, I. Nikolova, I. Temnikova, & I. Temnikova (Eds.),
International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings (pp. 1229-1239). Incoma Ltd. International Conference Recent Advances in Natural Language Processing, RANLP Vol. 2019-September
https://doi.org/10.26615/978-954-452-056-4_141
Vancouver
Vasileva S
, Atanasova P, Màrquez L, Barrón-Cedeño A, Nakov P.
It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction. In Angelova G, Mitkov R, Nikolova I, Temnikova I, Temnikova I, editors, International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings. Incoma Ltd. 2019. p. 1229-1239. (International Conference Recent Advances in Natural Language Processing, RANLP, Vol. 2019-September).
https://doi.org/10.26615/978-954-452-056-4_141
Author
Vasileva, Slavena ; Atanasova, Pepa ; Màrquez, Lluís ; Barrón-Cedeño, Alberto ; Nakov, Preslav. / It takes nine to smell a rat : Neural multi-task learning for check-worthiness prediction. International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings. editor / Galia Angelova ; Ruslan Mitkov ; Ivelina Nikolova ; Irina Temnikova ; Irina Temnikova. Incoma Ltd, 2019. pp. 1229-1239 (International Conference Recent Advances in Natural Language Processing, RANLP, Vol. 2019-September).
Bibtex
@inproceedings{2f1e9965d77f4b98b2797fd9d02f656a,
title = "It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction",
abstract = "We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.",
author = "Slavena Vasileva and Pepa Atanasova and Llu{\'i}s M{\`a}rquez and Alberto Barr{\'o}n-Cede{\~n}o and Preslav Nakov",
year = "2019",
doi = "10.26615/978-954-452-056-4_141",
language = "English",
series = "International Conference Recent Advances in Natural Language Processing, RANLP",
publisher = "Incoma Ltd",
pages = "1229--1239",
editor = "Galia Angelova and Ruslan Mitkov and Ivelina Nikolova and Irina Temnikova and Irina Temnikova",
booktitle = "International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings",
note = "12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 ; Conference date: 02-09-2019 Through 04-09-2019",
}
RIS
TY - GEN
T1 - It takes nine to smell a rat
T2 - 12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019
AU - Vasileva, Slavena
AU - Atanasova, Pepa
AU - Màrquez, Lluís
AU - Barrón-Cedeño, Alberto
AU - Nakov, Preslav
PY - 2019
Y1 - 2019
N2 - We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.
AB - We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.
UR - http://www.scopus.com/inward/record.url?scp=85070498498&partnerID=8YFLogxK
U2 - 10.26615/978-954-452-056-4_141
DO - 10.26615/978-954-452-056-4_141
M3 - Article in proceedings
AN - SCOPUS:85070498498
T3 - International Conference Recent Advances in Natural Language Processing, RANLP
SP - 1229
EP - 1239
BT - International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings
A2 - Angelova, Galia
A2 - Mitkov, Ruslan
A2 - Nikolova, Ivelina
A2 - Temnikova, Irina
A2 - Temnikova, Irina
PB - Incoma Ltd
Y2 - 2 September 2019 through 4 September 2019
ER -