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    • Alva-Manchego, F. (5)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 The (un)suitability of automatic evaluation metrics for text simplification Alva-Manchego, F.; Scarton, C.; Specia, L.. - : MIT Press, 2021 BASE Show details 2 Knowledge distillation for quality estimation Gajbhiye, A.; Fomicheva, M.; Alva-Manchego, F.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 3 ASSET : a dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations Alva-Manchego, F.; Martin, L.; Bordes, A.. - 2020 BASE Show details 4 Data-driven sentence simplification: Survey and benchmark Alva-Manchego, F.; Scarton, C.; Specia, L.. - : MIT Press, 2020 BASE Show details 5 Strong baselines for complex word identification across multiple languages ... Finnimore, P; Fritzsch, E; King, D. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 6 EASSE: easier automatic sentence simplification evaluation Alva-Manchego, F.; Martin, L.; Scarton, C.; Specia, L.. - : Association for Computational Linguistics, 2019 Abstract: We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic metrics for assessing SS outputs (e.g. SARI), word-level accuracy scores for certain simplification transformations, reference-independent quality estimation features (e.g. compression ratio), and standard test data for SS evaluation (e.g. TurkCorpus). Finally, EASSE generates easy-to-visualise reports on the various metrics and features above and on how a particular SS output fares against reference simplifications. Through experiments, we show that these functionalities allow for better comparison and understanding of the performance of SS systems. URL: http://eprints.whiterose.ac.uk/149948/ https://eprints.whiterose.ac.uk/149948/8/D19-3009.pdf BASE Hide details 7 Strong baselines for complex word identification across multiple languages Finnimore, P; Fritzsch, E; King, D. - : Association for Computational Linguistics, 2019. : Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019 BASE Show details

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