2 citations · 5 across the 27 of their papers we have counts for
15 papers · 1 filter
Implicit Causal Graph Construction in Text via Chain Discovery
Liesbeth Allein, Marie-Francine Moens
Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-…
NewsRECON: News Article Retrieval for Image Contextualization
Jonathan Tonglet, Iryna Gurevych, Tinne Tuytelaars +1
Identifying when and where a news image was taken is crucial for journalists and forensic experts to produce credible stories and debunk misinformation. While many existing methods…
Protecting multimodal large language models against misleading visualizations
Jonathan Tonglet, Tinne Tuytelaars, Marie-Francine Moens +1
Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understandi…
End-to-end Planner Training for Language Modeling
Nathan Cornille, Florian Mai, Jingyuan Sun +1
Through end-to-end training to predict the next token, LLMs have become valuable tools for various tasks. Enhancing their core training in language modeling can improve numerous do…
Learning to Plan Long-Term for Language Modeling
Florian Mai, Nathan Cornille, Marie-Francine Moens
Modern language models predict the next token in the sequence by considering the past text through a powerful function such as attention. However, language models have no explicit…
"Image, Tell me your story!" Predicting the original meta-context of visual misinformation
Jonathan Tonglet, Marie-Francine Moens, Iryna Gurevych
To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistenc…
Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-att…
Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework desi…
Learning to Plan for Language Modeling from Unlabeled Data
Nathan Cornille, Marie-Francine Moens, Florian Mai
By training to predict the next token in an unlabeled corpus, large language models learn to perform many tasks without any labeled data. However, their next-token-prediction objec…
Tuning In to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language
Jingyuan Sun, Xiaohan Zhang, Marie-Francine Moens
To understand the algorithm that supports the human brain's language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddi…
Sequence-to-Sequence Spanish Pre-trained Language Models
Vladimir Araujo, Maria Mihaela Trusca, Rodrigo Tufiño +1
In recent years, significant advancements in pre-trained language models have driven the creation of numerous non-English language variants, with a particular emphasis on encoder-o…
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leve…
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information
Vladimir Araujo, Alvaro Soto, Marie-Francine Moens
Existing question answering methods often assume that the input content (e.g., documents or videos) is always accessible to solve the task. Alternatively, memory networks were intr…
Generating multiple-choice questions for medical question answering with distractors and cue-masking
Damien Sileo, Kanimozhi Uma, Marie-Francine Moens
Medical multiple-choice question answering (MCQA) is particularly difficult. Questions may describe patient symptoms and ask for the correct diagnosis, which requires domain knowle…
Probing neural language models for understanding of words of estimative probability
Damien Sileo, Marie-Francine Moens
Words of estimative probability (WEP) are expressions of a statement's plausibility (probably, maybe, likely, doubt, likely, unlikely, impossible...). Multiple surveys demonstrate…