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Controllable Text Simplification with Explicit Paraphrasing ...
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The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification ...
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deepQuest-py: large and distilled models for quality estimation
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IAPUCP at SemEval-2021 task 1: Stacking fine-tuned transformers is almost all you need for lexical complexity prediction
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The (un)suitability of automatic evaluation metrics for text simplification
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deepQuest-py: large and distilled models for quality estimation
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021)
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Knowledge distillation for quality estimation
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In: 5091 ; 5099 (2021)
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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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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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Distributed knowledge based clinical auto-coding system
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Kaur, Rajvir (S33301). - : U.S., Association for Computational Linguistics, 2019
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Towards semi-supervised Brazilian Portuguese semantic role labeling: Building a benchmark
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