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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; Kriz, Reno; Maddela, Mounica; Xu, Wei. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.500/ Abstract: An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this `split and rephrase' task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37931-bisect-learning-to-split-and-rephrase-sentences-with-bitexts https://dx.doi.org/10.48448/98n6-k286 BASE Hide details 5 Controllable Text Simplification with Explicit Paraphrasing ... NAACL 2021 2021; Alva-Manchego, Fernando; Maddela, Mounica. - : Underline Science Inc., 2021 BASE Show 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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