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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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Abstract:
This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words. Our model leverages standard morphosyntactic and frequency-based features that proved helpful for Complex Word Identification (a related task), and combines them with predictions made by Transformer-based pre-trained models that were fine-tuned on the Shared Task data. Our submission system stacks all previous models with a LightGBM at the top. One novelty of our approach is the use of multi-task learning for fine-tuning a pre-trained model for both Lexical Complexity Prediction and Word Sense Disambiguation. Our analysis shows that all independent models achieve a good performance in the task, but that stacking them obtains a Pearson correlation of 0.7704, merely 0.018 points behind the winning submission.
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URL: https://doi.org/10.18653/v1/2021.semeval-1.14 https://orca.cardiff.ac.uk/147258/1/2021.semeval-1.14.pdf https://orca.cardiff.ac.uk/147258/
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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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