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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 Page: 1 2 Hits 1 – 20 of 32 1 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya... In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021) BASE Show details 2 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ... Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya. - : arXiv, 2021 BASE Show details 3 A Thorough Evaluation of Task-Specific Pretraining for Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Maynez, Joshua; Narayan, Shashi; Rothe, Sascha. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.12/ Abstract: Task-agnostic pretraining objectives like masked language models or corrupted span prediction are applicable to a wide range of NLP downstream tasks (Raffel et al., 2019), but are outperformed by task-specific pretraining objectives like predicting extracted gap sentences on summarization (Zhang et al., 2020). We compare three summarization specific pretraining objectives with the task agnostic corrupted span prediction pretraining in a controlled study. We also extend our study to a low resource and zero shot setup, to understand how many training examples are needed in order to ablate the task-specific pretraining without quality loss. Our results show that task-agnostic pretraining is sufficient for most cases which hopefully reduces the need for costly task-specific pretraining. We also report new state-of-the-art number for two summarization tasks using a T5 model with 11 billion parameters and an optimal beam search length ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization URL: https://dx.doi.org/10.48448/rxsd-pa23 https://underline.io/lecture/38078-a-thorough-evaluation-of-task-specific-pretraining-for-summarization BASE Hide details 4 MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Dusek, Ondrej; Konstas, Ioannis. - : Underline Science Inc., 2021 BASE Show details 5 Deep learning approaches to text production Narayan, Shashi; Gardent, Claire. - [San Rafael, California] : Morgan & Claypool Publishers, 2020 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details 6 Leveraging Pre-trained Checkpoints for Sequence Generation Tasks Rothe, Sascha; Narayan, Shashi; Severyn, Aliaksei In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 264-280 (2020) (2020) BASE Show details 7 Privacy-preserving Neural Representations of Text Coavoux, Maximin; Narayan, Shashi; Cohen, Shay B. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ; 2018 Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-02135081 ; 2018 Conference on Empirical Methods in Natural Language Processing, Nov 2018, Brussels, Belgium. pp.1--10 (2018) BASE Show details 8 Deep Learning Approaches to Text Production ... Gardent, Claire; Narayan, Shashi. - : Zenodo, 2018 BASE Show details 9 Deep Learning Approaches to Text Production ... Gardent, Claire; Narayan, Shashi. - : Zenodo, 2018 BASE Show details 10 Split and Rephrase Narayan, Shashi; Gardent, Claire; Cohen, Shay,... In: EMNLP 2017: Conference on Empirical Methods in Natural Language Processing ; https://hal.inria.fr/hal-01623746 ; EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhagen, Denmark. pp.617 - 627 (2017) BASE Show details 11 The WebNLG Challenge: Generating Text from RDF Data Gardent, Claire; Shimorina, Anastasia; Narayan, Shashi... In: Proceedings of the 10th International Conference on Natural Language Generation ; https://hal.archives-ouvertes.fr/hal-02461197 ; Proceedings of the 10th International Conference on Natural Language Generation, Sep 2017, Santiago de Compostela, Spain. pp.124-133, ⟨10.18653/v1/W17-3518⟩ (2017) BASE Show details 12 Creating Training Corpora for NLG Micro-Planning Gardent, Claire; Shimorina, Anastasia; Narayan, Shashi... In: 55th annual meeting of the Association for Computational Linguistics (ACL) ; https://hal.inria.fr/hal-01623744 ; 55th annual meeting of the Association for Computational Linguistics (ACL), Jul 2017, Vancouver, Canada (2017) BASE Show details 13 The Summa Platform Prototype ... Liepins, Renars; Germann, Ulrich; Barzdins, Guntis. - : Zenodo, 2017 BASE Show details 14 The Summa Platform Prototype ... Liepins, Renars; Germann, Ulrich; Barzdins, Guntis. - : Zenodo, 2017 BASE Show details 15 Creating Training Corpora For Micro-Planners ... Gardent, Claire; Shimorina, Anastasia; Narayan, Shashi. - : Zenodo, 2017 BASE Show details 16 Creating Training Corpora For Micro-Planners ... Gardent, Claire; Shimorina, Anastasia; Narayan, Shashi. - : Zenodo, 2017 BASE Show details 17 The WebNLG Challenge: Generating Text from RDF Data ... Gardent, Claire; Shimorina, Anastasia; Narayan, Shashi. - : Zenodo, 2017 BASE Show details 18 The WebNLG Challenge: Generating Text from RDF Data ... Gardent, Claire; Shimorina, Anastasia; Narayan, Shashi. - : Zenodo, 2017 BASE Show details 19 The SUMMA Platform Prototype Riedel, Sebastian; Garner, Phil; Narayan, Shashi... In: http://infoscience.epfl.ch/record/233575 (2017) BASE Show details 20 Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing ... Narayan, Shashi; Reddy, Siva; Cohen, Shay B.. - : arXiv, 2016 BASE Show details Page: 1 2

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