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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 – 5 of 5 1 Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity ... Harkous, Hamza; Groves, Isabel; Saffari, Amir. - : arXiv, 2020 Abstract: End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to pipeline-based architectures. However, it has faced challenges in generalizing to new domains and generating semantically consistent text. In this work, we present DataTuner, a neural, end-to-end data-to-text generation system that makes minimal assumptions about the data representation and the target domain. We take a two-stage generation-reranking approach, combining a fine-tuned language model with a semantic fidelity classifier. Each of our components is learnt end-to-end without the need for dataset-specific heuristics, entity delexicalization, or post-processing. We show that DataTuner achieves state of the art results on the automated metrics across four major D2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with a fluency assessed by human annotators nearing or exceeding the human-written reference texts. We further demonstrate that the model-based semantic fidelity scorer in DataTuner is a better ... : 28th International Conference on Computational Linguistics (COLING 2020), Online, December 8-13, 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2004.06577 https://arxiv.org/abs/2004.06577 BASE Hide details 2 Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity ... The 28th International Conference on Computational Linguistics 2020; Groves, Isabel; Harkous, Hamza. - : Underline Science Inc., 2020 BASE Show details 3 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-Level Classification Gupta, Amit; Lebret, Remi; Harkous, Hamza... In: http://infoscience.epfl.ch/record/271091 (2019) BASE Show details 4 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification ... Gupta, Amit; Lebret, Rémi; Harkous, Hamza. - : arXiv, 2017 BASE Show details 5 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification Gupta, Amit; Lebret, Rémi Philippe; Harkous, Hamza... In: http://infoscience.epfl.ch/record/227967 (2017) BASE Show details

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