Challenges in Data-to-Document Generation
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...(2) Computing the optimum argmax sequence from recurrent neural language models is not tractable, so consider two prominent decoding methods for approximating the argmax: Beam search is the most commonly used approximation in practice (Li et al., 2016c; Shen et al., 2017; Wiseman et al., 2017)....
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...Computing the optimum argmax sequence from recurrent neural language models is not tractable, so consider two prominent decoding methods for approximating the argmax: Beam search is the most commonly used approximation in practice (Li et al., 2016c; Shen et al., 2017; Wiseman et al., 2017)....
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Cites background from "Challenges in Data-to-Document Gene..."
...In contrast, tasks such as document summarization (Nenkova and McKeown, 2011; See et al., 2017; Paulus et al., 2018) and data-to-text generation (Lebret et al., 2016; Wiseman et al., 2017) which are not open-ended, require models to be factual and/or faithful to the source text....
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...Such divergence issue between source and target is not uncommon in conditional text generation (Kryscinski et al., 2019a; Wiseman et al., 2017; Dhingra et al., 2019)....
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..., 2018) and data-to-text generation (Lebret et al., 2016; Wiseman et al., 2017) which are not open-ended, require models to be factual and/or faithful to the source text....
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...Dhingra et al. (2019) proposed a new automatic metric, PARENT, for data-to-text generation (Lebret et al., 2016; Wiseman et al., 2017) which aligns n-grams from the reference and generated texts to the source table to measure the accuracy of n-grams that are entailed from the source table....
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...(2019) proposed a new automatic metric, PARENT, for data-to-text generation (Lebret et al., 2016; Wiseman et al., 2017) which aligns n-grams from the reference and generated texts to the source table to measure the accuracy of n-grams that are entailed from the source table....
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References
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"Challenges in Data-to-Document Gene..." refers methods in this paper
...As a base model we utilize the now standard attentionbased encoder-decoder model (Sutskever et al., 2014; Cho et al., 2014; Bahdanau et al., 2015)....
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