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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 Hits 1 – 8 of 8 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 BiSECT: Learning to Split and Rephrase Sentences with Bitexts ... Kim, Joongwon; Maddela, Mounica; Kriz, Reno. - : arXiv, 2021 BASE Show details 3 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ... Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya. - : arXiv, 2021 BASE Show details 4 BiSECT: Learning to Split and Rephrase Sentences with Bitexts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Callison-Burch, Chris; Kim, Joongwon. - : Underline Science Inc., 2021 BASE Show details 5 Controllable Text Simplification with Explicit Paraphrasing ... NAACL 2021 2021; Alva-Manchego, Fernando; Maddela, Mounica; Xu, Wei. - : Underline Science Inc., 2021 Abstract: Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.277/ Abstract: Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing ... Keyword: Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing URL: https://dx.doi.org/10.48448/ze3t-sa51 https://underline.io/lecture/19581-controllable-text-simplification-with-explicit-paraphrasing BASE Hide details 6 Controllable text simplification with explicit paraphrasing Maddela, Mounica; Alva-Manchego, Fernando; Xu, Wei. - : Association for Computational Linguistics, 2021 BASE Show details 7 Controllable Text Simplification with Explicit Paraphrasing ... Maddela, Mounica; Alva-Manchego, Fernando; Xu, Wei. - : arXiv, 2020 BASE Show details 8 A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification ... Maddela, Mounica; Xu, Wei. - : arXiv, 2018 BASE Show details

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