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arXiv 1908.07912cs.CLcs.AI

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

Sofia University（索非亚大学）；University of Copenhagen（哥本哈根大学）；Amazon Core ML（亚马逊核心机器学习部门）；Università di Bologna（博洛尼亚大学）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）

Slavena Vasileva, Pepa Atanasova, Lluís Màrquez, Alberto Barrón-Cedeño, Preslav Nakov

备注：
Check-worthiness; Fact-Checking; Veracity; Multi-task Learning; Neural Networks. arXiv admin note: text overlap with arXiv:1908.01328
发表信息：
RANLP-2019
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英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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