Towards a text analysis system for political debates

Mehrotra²,

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

et al. 2021

First and foremost, I am grateful to my three supervisors, Christina, Jakob, and Stephen, who have provided me with many lessons on both academia and science as a whole, as well as personal lessons I will keep forever. My weekly meetings with Christina and Jakob have always been a source of joy, as they were both scientifically enlightening but also simply entertaining. I would also like to thank my fellow PhD students, with whom I have worked during the years, Lucas, Dongsheng, Stephan, and Niklas.During my PhD, I have worked together with great people at Edulab to improve how mathematics is taught in Denmark. This has been an excellent collaboration where real-world problems could meet academia with exciting results. Of all the people at Edulab, I would especially like to thank Lotte, Kasper, Morten, Jakob, Stani, and Daniel.I was fortunate to spend a summer away from Denmark as an intern at Spotify Research in London. I got to work with many wonderful people, where I would especially like to thank Rishabh, Brian, Federico, Lucas, and Mounia.I also want to thank Rastin and Benjamin for a great collaboration at Danmarks Nationalbank. I realized how lucky I am that papers in computer science are so much shorter than in economics.From my personal life, I would first like to thank all my friends and family who have put up with my wacky work schedule during the last few years. Especially I would like to thank Mikkel, Helena, and David, who have been a great support. Lastly, I would like to thank the two most important people during my PhD, my twin brother Casper and my fiancée Spring. Casper has been a great source of inspiration in our work together and has been the best collaborator anyone could ever hope to work with. Spring has been a constant source of support and love, who not only accepts that I like to talk about my work, but also perfectly feigns interest in it.

Section: Chapter 8: Fact Check-worthiness Detection With Contrastive Rankingmentioning

confidence: 99%

Shifting Consumption towards Diverse Content on Music Streaming Platforms

Mehrotra²,

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

et al. 2021

Proceedings - Natural Language Processing in a Deep Learning World

“…Finally, Le et al (2016) used deep learning. They argued that the top terms in claim vs. nonclaim sentences are highly overlapping in content, which is a problem for bag-of-words approaches.…”

Section: Related Workmentioning

confidence: 99%

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

Vasileva¹,

Atanasova²,

Màrquez³

et al. 2019

We propose a multi-task deep-learning approach for estimating the checkworthiness 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 factchecking 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.

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

“…Thus, the task can be conformed as recognizing textual entailment, which is analyzed in detail in (Dagan et al, 2009). Finally, Le et al (2016) argued that the top terms in claim vs. non-claim sentences are highly overlapping, which is a problem for bag-of-words approaches. Thus, they used a Convolutional Neural Network, where each word is represented by its embedding and each named entity is replaced by its tag, e.g., person, organization, location.…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates

Gencheva¹,

Nakov²,

Màrquez³

et al. 2017

110

In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.