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SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding ...
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Improving Tokenisation by Alternative Treatment of Spaces ...
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NILC-Metrix: assessing the complexity of written and spoken language in Brazilian Portuguese ...
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Multistage BiCross encoder for multilingual access to COVID-19 health information ...
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Multistage BiCross encoder for multilingual access to COVID-19 health information
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In: PLoS One (2021)
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The (un)suitability of automatic evaluation metrics for text simplification
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889823 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States (2020)
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Abstract:
International audience ; 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 words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. 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.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.inria.fr/hal-02889823/file/ACL_2020___ASSET__A_Dataset_for_Tuning_and_Evaluation___.pdf https://hal.inria.fr/hal-02889823 https://hal.inria.fr/hal-02889823/document
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Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis ...
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
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Linguistic analysis model for monitoring user reaction on satirical news for brazilian portuguese
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ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
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Data-Driven Sentence Simplification: Survey and Benchmark
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In: Computational Linguistics, Vol 46, Iss 1, Pp 135-187 (2020) (2020)
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Text Simplification From Professionally Produced Corpora ...
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SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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Text Simplification From Professionally Produced Corpora ...
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SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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Sheffield submissions for the WMT18 quality estimation shared task
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In: 807 ; 813 (2018)
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