activity
20182021
most citedCategorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic

8 citations · 14 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CL2021

AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models

Harish Tayyar Madabushi, Edward Gow-Smith, Carolina Scarton +1

Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiwo…

cs.SI20218 cited

Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic

Ye Jiang, Xingyi Song, Carolina Scarton +2

The spreading COVID-19 misinformation over social media already draws the attention of many researchers. According to Google Scholar, about 26000 COVID-19 related misinformation st…

cs.CL2020

Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis

João A. Leite, Diego F. Silva, Kalina Bontcheva +1

Hate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capabl…

cs.CL2020

Measuring What Counts: The case of Rumour Stance Classification

Carolina Scarton, Diego F. Silva, Kalina Bontcheva

Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies towar…

cs.CL20203 cited

ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations

Fernando Alva-Manchego, Louis Martin, Antoine Bordes +3

In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex wo…

cs.CL20193 cited

Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality

Carolina Scarton, Mikel L. Forcada, Miquel Esplà-Gomis +1

Devising metrics to assess translation quality has always been at the core of machine translation (MT) research. Traditional automatic reference-based metrics, such as BLEU, have s…