no code implementations • RDSM (COLING) 2020 • Yue Li, Carolina Scarton
Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours.
no code implementations • EAMT 2022 • Sebastian T. Vincent, Loïc Barrault, Carolina Scarton
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
1 code implementation • CL (ACL) 2021 • Fernando Alva-Manchego, Carolina Scarton, Lucia Specia
Second, we conduct the first meta-evaluation of automatic metrics in Text Simplification, using our new data set (and other existing data) to analyze the variation of the correlation between metrics’ scores and human judgments across three dimensions: the perceived simplicity level, the system type, and the set of references used for computation.
no code implementations • 29 Mar 2023 • Sebastian Vincent, Rowanne Sumner, Alice Dowek, Charlotte Blundell, Emily Preston, Chris Bayliss, Chris Oakley, Carolina Scarton
Personalisation of language models for dialogue sensitises them to better capture the speaking patterns of people of specific characteristics, and/or in specific environments.
no code implementations • 22 Mar 2023 • Yue Li, Carolina Scarton
Considering a conversation thread, stance classification aims to identify the opinion (e. g. agree or disagree) of replies towards a given target.
no code implementations • 16 Mar 2023 • Ben Wu, Olesya Razuvayevskaya, Freddy Heppell, João A. Leite, Carolina Scarton, Kalina Bontcheva, Xingyi Song
For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining.
no code implementations • 17 Jan 2023 • Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song
Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.
1 code implementation • 18 Oct 2022 • Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton
Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.
Ranked #1 on
Lay Summarization
on PLOS
no code implementations • 18 Jul 2022 • Yue Li, Carolina Scarton, Xingyi Song, Kalina Bontcheva
One of the reasons behind this is vaccine disinformation which widely spreads in social media.
1 code implementation • SemEval (NAACL) 2022 • Iknoor Singh, Yue Li, Melissa Thong, Carolina Scarton
This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity.
no code implementations • LREC (MWE) 2022 • Dylan Phelps, Xuan-Rui Fan, Edward Gow-Smith, Harish Tayyar Madabushi, Carolina Scarton, Aline Villavicencio
In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection.
no code implementations • IWSLT (ACL) 2022 • Sebastian T. Vincent, Loïc Barrault, Carolina Scarton
This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign.
no code implementations • 10 May 2022 • Sebastian T. Vincent, Loïc Barrault, Carolina Scarton
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
1 code implementation • SemEval (NAACL) 2022 • Harish Tayyar Madabushi, Edward Gow-Smith, Marcos Garcia, Carolina Scarton, Marco Idiart, Aline Villavicencio
This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.
1 code implementation • 8 Apr 2022 • Edward Gow-Smith, Harish Tayyar Madabushi, Carolina Scarton, Aline Villavicencio
We find that our modified algorithms lead to improved performance on downstream NLP tasks that involve handling complex words, whilst having no detrimental effect on performance in general natural language understanding tasks.
1 code implementation • Findings (EMNLP) 2021 • Harish Tayyar Madabushi, Edward Gow-Smith, Carolina Scarton, Aline Villavicencio
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 multiword expressions (MWEs), especially idioms.
1 code implementation • ACL 2021 • Marcos Garcia, Tiago Kramer Vieira, Carolina Scarton, Marco Idiart, Aline Villavicencio
This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level.
no code implementations • 22 Jun 2021 • Ye Jiang, Xingyi Song, Carolina Scarton, Ahmet Aker, Kalina Bontcheva
In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).
1 code implementation • EACL 2021 • Marcos Garcia, Tiago Kramer Vieira, Carolina Scarton, Marco Idiart, Aline Villavicencio
Contextualised word representation models have been successfully used for capturing different word usages and they may be an attractive alternative for representing idiomaticity in language.
1 code implementation • 8 Jan 2021 • Iknoor Singh, Carolina Scarton, Kalina Bontcheva
The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Carolina Scarton, Diego F. Silva, Kalina Bontcheva
This paper specifically questions the evaluation metrics used in these shared tasks.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • João A. Leite, Diego F. Silva, Kalina Bontcheva, Carolina Scarton
Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media.
Ranked #1 on
Hate Speech Detection
on ToLD-Br
1 code implementation • ACL 2020 • Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoît Sagot, Lucia Specia
Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
no code implementations • LREC 2020 • Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, Carolina Scarton
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health.
no code implementations • CL 2020 • Fern Alva-Manchego, o, Carolina Scarton, Lucia Specia
Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand.
1 code implementation • EMNLP (IWSLT) 2019 • Carolina Scarton, Mikel L. Forcada, Miquel Esplà-Gomis, Lucia Specia
To that end, we report experiments on a dataset with newly-collected post-editing indicators and show their usefulness when estimating post-editing effort.
1 code implementation • IJCNLP 2019 • Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems.
no code implementations • WS 2019 • Fern Alva-Manchego, o, Carolina Scarton, Lucia Specia
Current approaches to Text Simplification focus on simplifying sentences individually.
no code implementations • WS 2018 • Julia Ive, Carolina Scarton, Fr{\'e}d{\'e}ric Blain, Lucia Specia
In this paper we present the University of Sheffield submissions for the WMT18 Quality Estimation shared task.
no code implementations • WS 2018 • Chiraag Lala, Pranava Swaroop Madhyastha, Carolina Scarton, Lucia Specia
For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.
no code implementations • WS 2018 • Mikel L. Forcada, Carolina Scarton, Lucia Specia, Barry Haddow, Alexandra Birch
A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language.
no code implementations • ACL 2018 • Carolina Scarton, Lucia Specia
Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text.
1 code implementation • IJCNLP 2017 • Fern Alva-Manchego, o, Joachim Bingel, Gustavo Paetzold, Carolina Scarton, Lucia Specia
Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data.
Ranked #8 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
no code implementations • IJCNLP 2017 • Carolina Scarton, Alessio Palmero Aprosio, Sara Tonelli, Tamara Mart{\'\i}n Wanton, Lucia Specia
Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications).
no code implementations • EACL 2017 • Yvette Graham, Qingsong Ma, Timothy Baldwin, Qun Liu, Carla Parra, Carolina Scarton
Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable.
no code implementations • COLING 2016 • Carolina Scarton, Gustavo Paetzold, Lucia Specia
The goal of QE is to estimate the quality of language output applications without the need of human references.
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri
1 code implementation • LREC 2016 • Carolina Scarton, Lucia Specia
Effectively assessing Natural Language Processing output tasks is a challenge for research in the area.
no code implementations • WS 2015 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, Marco Turchi