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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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Controllable Text Simplification with Explicit Paraphrasing ...
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
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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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Automatic Sentence Simplification with Multiple Rewriting Transformations
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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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Abstract:
One of the main research challenges in semantic role labeling (SRL) is the development of applications for languages other than English. For Brazilian Portuguese, recent projects in lexical semantics are about to provide the necessary computational resources for research in this area. However, the amount of annotated data provided is not significant enough for successful supervised learning. Hence, we propose to use a semi-supervised approach capable of taking advantage of both annotated and unannotated data available. In this paper, we outline the methodology for the development of this SRL system, the same as the benchmark to be used to test its performance.
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URL: https://doi.org/10.1007/978-3-642-28885-2_24 https://orca.cardiff.ac.uk/147266/
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