---
title: "Who cites The WebNLG Challenge: Generating Text from RDF…"
description: "The papers of the Elamur Research library that cite «The WebNLG Challenge: Generating Text from RDF Data», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/gardent-2017-webnlg-challenge-generating-text-rdf/cited-by/page/2/"
---
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# Who cites «The WebNLG Challenge: Generating Text from RDF Data» · page 2

237 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/gardent-2017-webnlg-challenge-generating-text-rdf/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/gardent-2017-webnlg-challenge-generating-text-rdf/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/gardent-2017-webnlg-challenge-generating-text-rdf/cited-by/page/3/)

1. [Block Diagram-to-Text: Understanding Block Diagram Images by Generating Natural Language Descriptors](https://aclanthology.org/2022.findings-aacl.15/) *— 2022 · Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022*
2. [Control Prefixes for Parameter-Efficient Text Generation](https://aclanthology.org/2022.gem-1.31/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
3. [Deep Learning for Text Style Transfer: A Survey](https://aclanthology.org/2022.cl-1.6/) *— 2022 · Computational Linguistics*
4. [DeepStruct: Pretraining of Language Models for Structure Prediction](https://aclanthology.org/2022.findings-acl.67/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
5. [EchoGen: Generating Conclusions from Echocardiogram Notes](https://aclanthology.org/2022.bionlp-1.35/) *— 2022 · Proceedings of the 21st Workshop on Biomedical Language Processing*
6. [Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention](https://aclanthology.org/2022.findings-naacl.102/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
7. [Fine-Grained Controllable Text Generation Using Non-Residual Prompting](https://aclanthology.org/2022.acl-long.471/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation](https://aclanthology.org/2022.acl-long.93/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://aclanthology.org/2022.coling-1.506/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
10. [GEMv2: Multilingual NLG Benchmarking in a Single Line of Code](https://aclanthology.org/2022.emnlp-demos.27/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
11. [Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies](https://aclanthology.org/2022.acl-long.586/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Generating Questions from Wikidata Triples](https://aclanthology.org/2022.lrec-1.29/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
13. [Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation](https://aclanthology.org/2022.findings-emnlp.547/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
14. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
15. [MReD: A Meta-Review Dataset for Structure-Controllable Text Generation](https://aclanthology.org/2022.findings-acl.198/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
16. [Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation](https://aclanthology.org/2022.tacl-1.72/) *— 2022 · Transactions of the Association for Computational Linguistics*
17. [Multi-task Active Learning for Pre-trained Transformer-based Models](https://aclanthology.org/2022.tacl-1.70/) *— 2022 · Transactions of the Association for Computational Linguistics*
18. [Neural Pipeline for Zero-Shot Data-to-Text Generation](https://aclanthology.org/2022.acl-long.271/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis](https://aclanthology.org/2022.findings-emnlp.489/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
20. [Open Domain Question Answering with A Unified Knowledge Interface](https://aclanthology.org/2022.acl-long.113/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [OpenPrompt: An Open-source Framework for Prompt-learning](https://aclanthology.org/2022.acl-demo.10/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
22. [Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation](https://aclanthology.org/2022.findings-naacl.108/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
23. [Pre-training to Match for Unified Low-shot Relation Extraction](https://aclanthology.org/2022.acl-long.397/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices](https://aclanthology.org/2022.ecnlp-1.13/) *— 2022 · Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*
25. [R2D2: Robust Data-to-Text with Replacement Detection](https://aclanthology.org/2022.emnlp-main.464/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
26. [Self-supervised Graph Masking Pre-training for Graph-to-Text Generation](https://aclanthology.org/2022.emnlp-main.321/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
27. [Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency](https://aclanthology.org/2022.findings-naacl.95/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
28. [Text-to-Text Extraction and Verbalization of Biomedical Event Graphs](https://aclanthology.org/2022.coling-1.238/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
29. [VisToT: Vision-Augmented Table-to-Text Generation](https://aclanthology.org/2022.emnlp-main.675/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
30. [What Makes Data-to-Text Generation Hard for Pretrained Language Models?](https://aclanthology.org/2022.gem-1.50/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
31. [uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation](https://aclanthology.org/2022.findings-acl.223/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
32. [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075) *— 2021 · arXiv:2105.03075*
33. [AggGen: Ordering and Aggregating while Generating](https://aclanthology.org/2021.acl-long.113/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
34. [Biomedical Data-to-Text Generation via Fine-Tuning Transformers](https://aclanthology.org/2021.inlg-1.40/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
35. [Building Adaptive Acceptability Classifiers for Neural NLG](https://aclanthology.org/2021.emnlp-main.53/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
36. [DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation](https://aclanthology.org/2021.acl-long.501/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
37. [Enriching the E2E dataset](https://aclanthology.org/2021.inlg-1.18/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
38. [Evaluating Text Generation from Discourse Representation Structures](https://md.research.elamur.ai/free/en/papers/wang-2021-evaluating-text-generation-discourse-representation/) *— our card · 2021 · Proceedings of the First Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
39. [Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation](https://aclanthology.org/2021.unimplicit-1.3/) *— 2021 · Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language*
40. [Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models](https://aclanthology.org/2021.findings-acl.136/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
41. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
42. [Focused Attention Improves Document-Grounded Generation](https://aclanthology.org/2021.naacl-main.338/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
43. [Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation](https://aclanthology.org/2021.acl-long.10/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
44. [Incremental Beam Manipulation for Natural Language Generation](https://aclanthology.org/2021.eacl-main.219/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
45. [Investigating Pretrained Language Models for Graph-to-Text Generation](https://aclanthology.org/2021.nlp4convai-1.20/) *— 2021 · Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI*
46. [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://aclanthology.org/2021.findings-acl.223/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
47. [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) *— 2021 · arXiv:2106.09685*
48. [Logic-Consistency Text Generation from Semantic Parses](https://aclanthology.org/2021.findings-acl.388/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
49. [Mention Flags (MF): Constraining Transformer-based Text Generators](https://aclanthology.org/2021.acl-long.9/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
50. [Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs](https://aclanthology.org/2021.textgraphs-1.2/) *— 2021 · Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)*
51. [NUIG-DSI's submission to The GEM Benchmark 2021](https://aclanthology.org/2021.gem-1.13/) *— 2021 · Proceedings of the First Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
52. [Neural Data-to-Text Generation with LM-based Text Augmentation](https://aclanthology.org/2021.eacl-main.64/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
53. [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://aclanthology.org/2021.findings-emnlp.76/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
54. [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
55. [Promoting Graph Awareness in Linearized Graph-to-Text Generation](https://aclanthology.org/2021.findings-acl.82/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
56. [SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation](https://aclanthology.org/2021.inlg-1.21/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
57. [Stage-wise Fine-tuning for Graph-to-Text Generation](https://aclanthology.org/2021.acl-srw.2/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop*
58. [Structural Adapters in Pretrained Language Models for AMR-to-Text Generation](https://aclanthology.org/2021.emnlp-main.351/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
59. [Structure-Aware Pre-Training for Table-to-Text Generation](https://aclanthology.org/2021.findings-acl.200/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
60. [Stylistic MR-to-Text Generation Using Pre-trained Language Models](https://aclanthology.org/2021.icon-main.13/) *— 2021 · Proceedings of the 18th International Conference on Natural Language Processing (ICON)*
61. [TWT: Table with Written Text for Controlled Data-to-Text Generation](https://aclanthology.org/2021.findings-emnlp.107/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
62. [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://aclanthology.org/2021.gem-1.10/) *— 2021 · Proceedings of the First Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
63. [Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR](https://md.research.elamur.ai/free/en/papers/opitz-2021-decomposable-metric-explainable-evaluation-text/) *— our card · 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
64. [Transforming Multi-Conditioned Generation from Meaning Representation](https://aclanthology.org/2021.ranlp-1.92/) *— 2021 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)*
65. [Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks](https://arxiv.org/abs/2111.10545) *— 2021 · arXiv:2111.10545*
66. [Uncovering Main Causalities for Long-tailed Information Extraction](https://aclanthology.org/2021.emnlp-main.763/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
67. [Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity](https://md.research.elamur.ai/free/en/papers/opitz-2021-weisfeiler-leman-bamboo-novel-amr/) *— our card · 2021 · Transactions of the Association for Computational Linguistics*
68. [WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset](https://aclanthology.org/2021.textgraphs-1.7/) *— 2021 · Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)*
69. [WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections](https://aclanthology.org/2021.findings-acl.17/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
70. [$\mathcalP^2$: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation](https://aclanthology.org/2020.webnlg-1.10/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
71. [A Case Study of NLG from Multimedia Data Sources: Generating Architectural Landmark Descriptions](https://aclanthology.org/2020.webnlg-1.1/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
72. [A General Benchmarking Framework for Text Generation](https://aclanthology.org/2020.webnlg-1.3/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
73. [An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing](https://aclanthology.org/2020.emnlp-main.577/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
74. [BLEURT: Learning Robust Metrics for Text Generation](https://aclanthology.org/2020.acl-main.704/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
75. [Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data](https://aclanthology.org/2020.coling-industry.7/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Industry Track*
76. [Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation](https://aclanthology.org/2020.acl-main.224/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
77. [CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training](https://aclanthology.org/2020.webnlg-1.8/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
78. [DART: Open-Domain Structured Data Record to Text Generation](https://arxiv.org/abs/2007.02871) *— 2020 · arXiv:2007.02871*
79. [DORB: Dynamically Optimizing Multiple Rewards with Bandits](https://aclanthology.org/2020.emnlp-main.625/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
80. [Data-to-Text Generation with Iterative Text Editing](https://aclanthology.org/2020.inlg-1.9/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
81. [Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers](https://aclanthology.org/2020.webnlg-1.9/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
82. [Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing](https://aclanthology.org/2020.inlg-1.24/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
83. [Dynamic Topic Tracker for KB-to-Text Generation](https://aclanthology.org/2020.coling-main.215/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
84. [ENT-DESC: Entity Description Generation by Exploring Knowledge Graph](https://aclanthology.org/2020.emnlp-main.90/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
85. [Enhancing Sequence-to-Sequence Modelling for RDF triples to Natural Text](https://aclanthology.org/2020.webnlg-1.5/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
86. [Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference](https://aclanthology.org/2020.inlg-1.19/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
87. [Evaluation of Text Generation: A Survey](https://arxiv.org/abs/2006.14799) *— 2020 · arXiv:2006.14799*
88. [Evaluation rules! On the use of grammars and rule-based systems for NLG evaluation](https://aclanthology.org/2020.evalnlgeval-1.3/) *— 2020 · Proceedings of the 1st Workshop on Evaluating NLG Evaluation*
89. [Few-Shot NLG with Pre-Trained Language Model](https://aclanthology.org/2020.acl-main.18/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
90. [GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation](https://aclanthology.org/2020.coling-main.217/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
91. [Gradations of Error Severity in Automatic Image Descriptions](https://aclanthology.org/2020.inlg-1.45/) *— 2020 · Proceedings of the 13th International Conference on Natural Language Generation*
92. [Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task](https://aclanthology.org/2020.webnlg-1.11/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
93. [Knowledge Graphs](https://arxiv.org/abs/2003.02320) *— 2020 · arXiv:2003.02320 (ACM Comput. Surv. 54(4): 71:1-71:37 (2021))*
94. [Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task](https://aclanthology.org/2020.wmt-1.102/) *— 2020 · Proceedings of the Fifth Conference on Machine Translation*
95. [Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks](https://aclanthology.org/2020.acl-main.67/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
96. [Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing](https://aclanthology.org/2020.webnlg-1.13/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
97. [Multilingual AMR-to-Text Generation](https://aclanthology.org/2020.emnlp-main.231/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
98. [NILC at WebNLG+: Pretrained Sequence-to-Sequence Models on RDF-to-Text Generation](https://aclanthology.org/2020.webnlg-1.14/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
99. [NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation](https://aclanthology.org/2020.webnlg-1.15/) *— 2020 · Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)*
100. [Online Back-Parsing for AMR-to-Text Generation](https://aclanthology.org/2020.emnlp-main.92/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*

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