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nlp:generation

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nlp:generation [2021/10/09 20:37] – jmflanignlp:generation [2024/08/16 00:57] (current) – jmflanig
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   * [[https://core.ac.uk/download/pdf/188246925.pdf|Belz 2008 - Automatic Generation of Weather Forecast Texts Using Comprehensive Probabilistic Generation-Space Models]]   * [[https://core.ac.uk/download/pdf/188246925.pdf|Belz 2008 - Automatic Generation of Weather Forecast Texts Using Comprehensive Probabilistic Generation-Space Models]]
   * [[https://arxiv.org/pdf/1809.00582.pdf|Puduppully et al 2018 - Data-to-Text Generation with Content Selection and Planning]] A good baseline, used as baseline in Workshop in NLG and Translation   * [[https://arxiv.org/pdf/1809.00582.pdf|Puduppully et al 2018 - Data-to-Text Generation with Content Selection and Planning]] A good baseline, used as baseline in Workshop in NLG and Translation
 +  * [[https://arxiv.org/pdf/2205.11055.pdf|Zhang et al 2022 - TempLM: Distilling Language Models into Template-Based Generators]]
  
 ===== Meaning-to-Text ===== ===== Meaning-to-Text =====
 This is generation from a meaning representation, such as [[Abstract Meaning Representation|AMR]] or slots and values - the inverse of [[semantic parsing]].  See also [[Abstract Meaning Representation#Generation|AMR - Generation]]. This is generation from a meaning representation, such as [[Abstract Meaning Representation|AMR]] or slots and values - the inverse of [[semantic parsing]].  See also [[Abstract Meaning Representation#Generation|AMR - Generation]].
 +
 +===== Controllable Text Generation =====
 +  * Overviews
 +    * [[https://lilianweng.github.io/posts/2021-01-02-controllable-text-generation/|Controllable Neural Text Generation]]
  
 ===== Evaluation ===== ===== Evaluation =====
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   * [[https://arxiv.org/pdf/2004.04696.pdf|Sallam et al 2020 - BLEURT: Learning Robust Metrics for Text Generation]] (ACL 2020)   * [[https://arxiv.org/pdf/2004.04696.pdf|Sallam et al 2020 - BLEURT: Learning Robust Metrics for Text Generation]] (ACL 2020)
   * [[https://arxiv.org/pdf/2102.01672.pdf|Gehrmann et al 2021 - The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics]] ([[https://gem-benchmark.com/|Website]])   * [[https://arxiv.org/pdf/2102.01672.pdf|Gehrmann et al 2021 - The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics]] ([[https://gem-benchmark.com/|Website]])
 +    * [[https://aclanthology.org/2021.tacl-1.87.pdf|Freitag et al 2021 - Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation]] Uses Multidimensional Quality Metrics (MQM) framework. MT paper, used in [[https://www2.statmt.org/wmt24/metrics-task.html|WMT]]
 +  * [[https://arxiv.org/pdf/2107.01294.pdf|Dou et al 2021 - Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text]]
 +  * [[https://arxiv.org/pdf/2406.07935|Ruan et al 2024 - Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation]]
 +
 +===== Historical Papers =====
 +Papers from a while ago.
 +
 +  * [[https://aclanthology.org/C10-1012.pdf|Bohnet et al 2010 - Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer]]
  
 ===== Datasets ===== ===== Datasets =====
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 ===== Related Pages ===== ===== Related Pages =====
-  * [[Seq2seq]]+  * [[Seq2seq|Sequence to Sequence Models]] 
 +  * [[Watermarking]]
  
nlp/generation.1633811833.txt.gz · Last modified: 2023/06/15 07:36 (external edit)
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