Search Results for author: Liesbeth Allein

Found 7 papers, 1 papers with code

When Do Discourse Markers Affect Computational Sentence Understanding?

no code implementations1 Sep 2023 RuiQi Li, Liesbeth Allein, Damien Sileo, Marie-Francine Moens

The capabilities and use cases of automatic natural language processing (NLP) have grown significantly over the last few years.

Sentence

Implicit Temporal Reasoning for Evidence-Based Fact-Checking

1 code implementation24 Feb 2023 Liesbeth Allein, Marlon Saelens, Ruben Cartuyvels, Marie-Francine Moens

Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.

Claim Verification Fact Checking

Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection

no code implementations31 Aug 2021 Liesbeth Allein, Marie-Francine Moens, Domenico Perrotta

The latent representations of news articles and user-generated content allow that during training the model is guided by the profile of users who prefer content similar to the news article that is evaluated, and this effect is reinforced if that content is shared among different users.

Model Optimization

Checkworthiness in Automatic Claim Detection Models: Definitions and Analysis of Datasets

no code implementations20 Aug 2020 Liesbeth Allein, Marie-Francine Moens

Public, professional and academic interest in automated fact-checking has drastically increased over the past decade, with many aiming to automate one of the first steps in a fact-check procedure: the selection of so-called checkworthy claims.

Fact Checking

Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction

no code implementations9 Jan 2020 Liesbeth Allein, Artuur Leeuwenberg, Marie-Francine Moens

Drawing on previous research conducted on neural context-dependent dt-mistake correction models (Heyman et al. 2018), this study constructs the first neural network model for Dutch demonstrative and relative pronoun resolution that specifically focuses on the correction and part-of-speech prediction of these two pronouns.

Binary Classification General Classification +3

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