43 citations · 86 across the 15 of their papers we have counts for
15 papers
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
Tomas Goldsack, Zheheng Luo, Qianqian Xie +4
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this sh…
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation
Sebastian Vincent, Robert Flynn, Carolina Scarton
Efficient utilisation of both intra- and extra-textual context remains one of the critical gaps between machine and human translation. Existing research has primarily focused on pr…
Reference-less Analysis of Context Specificity in Translation with Personalised Language Models
Sebastian Vincent, Alice Dowek, Rowanne Sumner +5
Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular env…
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
Tomas Goldsack, Zhihao Zhang, Chenghua Lin +1
Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts. Automatic approaches for lay summarisation can pr…
NILC-Metrix: assessing the complexity of written and spoken language in Brazilian Portuguese
Sidney Evaldo Leal, Magali Sanches Duran, Carolina Evaristo Scarton +2
This paper presents and makes publicly available the NILC-Metrix, a computational system comprising 200 metrics proposed in studies on discourse, psycholinguistics, cognitive and c…
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…
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…
The False COVID-19 Narratives That Keep Being Debunked: A Spatiotemporal Analysis
Iknoor Singh, Kalina Bontcheva, Carolina Scarton
The onset of the COVID-19 pandemic led to a global infodemic that has brought unprecedented challenges for citizens, media, and fact-checkers worldwide. To address this challenge,…
Multistage BiCross encoder for multilingual access to COVID-19 health information
Iknoor Singh, Carolina Scarton, Kalina Bontcheva
The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online. This has motivated the need for accurate semantic search and retrieval of…
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…
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…
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…
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…
EASSE: Easier Automatic Sentence Simplification Evaluation
Fernando Alva-Manchego, Louis Martin, Carolina Scarton +1
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single acces…
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting
Mikel L. Forcada, Carolina Scarton, Lucia Specia +2
A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language. Evaluation of the usefulness of MT for gisting is su…