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Hits 1 – 15 of 15

1
Improving tokenisation by alternative treatment of spaces
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2
The (un)suitability of automatic evaluation metrics for text simplification
Alva-Manchego, F.; Scarton, C.; Specia, L.. - : MIT Press, 2021
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3
Assessing idiomaticity representations in vector models with a noun compound dataset labeled at type and token levels
Garcia, M.; Kramer Vieira, T.; Scarton, C.. - : Association for Computational Linguistics (ACL), 2021
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4
Probing for idiomaticity in vector space models
Garcia, M.; Vieira, T.K.; Scarton, C.. - : Association for Computational Linguistics (ACL), 2021
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5
Multistage BiCross encoder for multilingual access to COVID-19 health information
Singh, I.; Scarton, C.; Bontcheva, K.. - : Public Library of Science, 2021
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6
The false COVID-19 narratives that keep being debunked : a spatiotemporal analysis
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7
AStitchInLanguageModels : dataset and methods for the exploration of idiomaticity in pre-trained language models
Tayyar Madabushi, H.; Gow-Smith, E.; Scarton, C.. - : Association for Computational Linguistics, 2021
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8
ASSET : a dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
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9
Toxic language detection in social media for Brazilian Portuguese : new dataset and multilingual analysis
Leite, J.A.; Silva, D.F.; Bontcheva, K.. - : Association for Computational Linguistics (ACL), 2020
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10
Measuring what counts : the case of rumour stance classification
Scarton, C.; Silva, D.F.; Bontcheva, K.. - : Association for Computational Linguistics (ACL), 2020
Abstract: 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 towards a given rumour, namely support, deny, question, or comment. Numerous methods have been proposed and their performance compared in the RumourEval shared tasks in 2017 and 2019. Results demonstrated that this is a challenging problem since naturally occurring rumour stance data is highly imbalanced. This paper specifically questions the evaluation metrics used in these shared tasks. We re-evaluate the systems submitted to the two RumourEval tasks and show that the two widely adopted metrics – accuracy and macro-F1 – are not robust for the four-class imbalanced task of rumour stance classification, as they wrongly favour systems with highly skewed accuracy towards the majority class. To overcome this problem, we propose new evaluation metrics for rumour stance detection. These are not only robust to imbalanced data but also score higher systems that are capable of recognising the two most informative minority classes (support and deny).
URL: https://eprints.whiterose.ac.uk/169795/7/2020.aacl-main.92.pdf
https://www.aclweb.org/anthology/2020.aacl-main.92
http://eprints.whiterose.ac.uk/169795/
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11
Data-driven sentence simplification: Survey and benchmark
Alva-Manchego, F.; Scarton, C.; Specia, L.. - : MIT Press, 2020
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12
EASSE: easier automatic sentence simplification evaluation
Alva-Manchego, F.; Martin, L.; Scarton, C.. - : Association for Computational Linguistics, 2019
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13
Exploring gap filling as a cheaper alternative to reading comprehension questionnaires when evaluating machine translation for gisting
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14
Multi-level translation quality prediction with QuEst++
Specia, L.; Paetzold, G.H.; Scarton, C.. - : Association for Computational Linguistics (ACL), 2015
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15
Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis
Toledo, C.M.; Cunha, A.; Scarton, C.. - : Associação Neurologia Cognitiva e do Comportamento, 2014
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