fact-checking

2 papers by CopeNLU authors are accepted to appear at EMNLP 2022, which are on scholarly document understanding. Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection. Indira Sen, Mattia Samory, Claudia Wagner, Isabelle Augenstein. Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings. Malte Ostendorff, Nils Rethmeier, Isabelle Augenstein, Bela Gipp, Georg Rehm.

3 papers by CopeNLU authors are accepted to appear at NAACL 2022, which are on the topics of hatespeech detection, misinformation detection and multilingual probing. Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection. Indira Sen, Mattia Samory, Claudia Wagner, Isabelle Augenstein. A Survey on Stance Detection for Mis- and Disinformation Identification. Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein. Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models.

2 papers by CopeNLU authors are accepted to appear at AAAI 2022. One paper is on explanation generation, demonstrating how directly optimising for diagnostic properties of explanations, such as faithfulness, data consistency and confidence indication, can improve explanation quality. The other paper presents the most comprehensive study of cross-lingual stance detection to date, and proposes methods for learning with limited labelled data across languages and domains. Diagnostics-Guided Explanation Generation. Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein.

A paper by CopeNLU author is accepted to appear at IJCAI 2021. The paper studies how to perform complex claim verification on naturally occurring political claims with multiple hops over evidence chunks. Multi-Hop Fact Checking of Political Claims. Wojciech Ostrowski, Arnav Arora, Pepa Atanasova, Isabelle Augenstein.

Determine the attitude expressed in a text towards a topic, and use this for automatic evidence-based fact checking

2 papers by CopeNLU authors are accepted to appear at ACL 2020. One paper is on explainable fact checking, providing the first study of how fact checking explanations can be generated automatically based on claim content, and how this task can be modelled jointly with veracity prediction; whereas the other one is on script conversion, proposing a novel Chinese character conversion model that can disambiguate between mappings and convert between Chinese scripts.