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--- nlp:generation [2021/08/20 22:56] – [Data-to-Text] jmflanig
+++ nlp:generation [2024/08/16 00:57] (current) – jmflanig
@@ Line 11: / Line 11: @@
   * [[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/2205.11055.pdf|Zhang et al 2022 - TempLM: Distilling Language Models into Template-Based Generators]]
 ===== 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]].
+===== Controllable Text Generation =====
+  * Overviews
+    * [[https://lilianweng.github.io/posts/2021-01-02-controllable-text-generation/|Controllable Neural Text Generation]]
 ===== Evaluation =====
 Survey: [[https://arxiv.org/pdf/2006.14799.pdf|2020 - Evaluation of Text Generation: A Survey]]
+  * [[https://aclanthology.org/E06-1040.pdf|Belz & Reiter 2006 - Comparing Automatic and Human Evaluation of NLG systems]]
   * [[https://www.aclweb.org/anthology/N19-1169.pdf|Hashimoto et al 2019 - Unifying Human and Statistical Evaluation for Natural Language Generation]]
   * [[https://www.aclweb.org/anthology/W19-8644.pdf|Dusek et al 2019 - Automatic Quality Estimation for Natural Language Generation:Ranting (Jointly Rating and Ranking)]]
   * [[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://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 =====
@@ Line 37: / Line 52: @@
   * [[https://scholar.google.com/citations?user=trwwiW4AAAAJ&hl=en|Anya Belz]]
   * [[https://scholar.google.com/citations?user=Ns0YuP0AAAAJ&hl=en|Ehud Reiter]]
+===== Related Pages =====
+  * [[Seq2seq|Sequence to Sequence Models]]
+  * [[Watermarking]]