4 citations · 4 across the 4 of their papers we have counts for
6 papers
Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
Wei Sun, Mingxiao Li, Damien Sileo +2
Medical Question Answering~(medical QA) systems play an essential role in assisting healthcare workers in finding answers to their questions. However, it is not sufficient to merel…
When Do Discourse Markers Affect Computational Sentence Understanding?
Ruiqi Li, Liesbeth Allein, Damien Sileo +1
The capabilities and use cases of automatic natural language processing (NLP) have grown significantly over the last few years. While much work has been devoted to understanding ho…
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…
Analysis and Prediction of NLP Models Via Task Embeddings
Damien Sileo, Marie-Francine Moens
Task embeddings are low-dimensional representations that are trained to capture task properties. In this paper, we propose MetaEval, a collection of NLP tasks. We fit a singl…
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features
Erfan Ghadery, Damien Sileo, Marie-Francine Moens
We describe our approach for SemEval-2021 task 6 on detection of persuasion techniques in multimodal content (memes). Our system combines pretrained multimodal models (CLIP) and ch…