---
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/"
---
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# Who cites «The WebNLG Challenge: Generating Text from RDF Data»

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

1. [Extending First-Order Logic for Factual Reasoning over Knowledge Graphs](https://aclanthology.org/2026.acl-long.1230/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Grouped Adaptive Weight Sharing (GAWS): An Inference-Efficient Adaptation Method for Large Language Models](https://aclanthology.org/2026.findings-acl.1590/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
3. [LLM Multi-Agent Systems for Long Triple Set Data-to-Text Generation](https://aclanthology.org/2026.findings-acl.1712/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
4. [Mapping Out the NLP Evaluation Landscape with a Standard Taxonomy of Quality Criteria](https://aclanthology.org/2026.gem-main.77/) *— 2026 · Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)*
5. [ReproHum \#0124-03: Reproducing Human Scores on Neural REG Models](https://aclanthology.org/2026.gem-main.86/) *— 2026 · Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)*
6. [The Shared Task on Reproducibility of Evaluations in NLP (ReproNLP) 2026: Overview and Results](https://aclanthology.org/2026.gem-main.83/) *— 2026 · Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)*
7. [Thesis Proposal: Intentional Inference for Insight Generation](https://aclanthology.org/2026.acl-srw.109/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
8. [Towards TEE-Certified DP: Verifiable Differentially Private Training on Legacy GPUs](https://arxiv.org/abs/2609.20532) *— 2026 · arXiv:2609.20532*
9. [Analysing Reference Production of Large Language Models](https://aclanthology.org/2025.inlg-main.12/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference*
10. [Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines](https://aclanthology.org/2025.findings-acl.176/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
11. [DCU-ADAPT-modPB at the GEM”24 Data-to-Text Task: Analysis of Human Evaluation Results](https://aclanthology.org/2025.inlg-genchal.3/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference: Generation Challenges*
12. [Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning](https://aclanthology.org/2025.findings-acl.1374/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
13. [Evaluating and Improving Graph to Text Generation with Large Language Models](https://aclanthology.org/2025.naacl-long.513/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
14. [Fact Verification on Knowledge Graph via Programmatic Graph Reasoning](https://aclanthology.org/2025.findings-emnlp.293/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
15. [Fine-Tuning, Prompting and RAG for Knowledge Graph-to-Russian Text Generation. How do these Methods generalise to Out-of-Distribution Data?](https://aclanthology.org/2025.inlg-main.26/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference*
16. [Graph-Guided Textual Explanation Generation Framework](https://aclanthology.org/2025.emnlp-main.1494/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
17. [Improving Factuality with Explicit Working Memory](https://aclanthology.org/2025.acl-long.548/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [Incorporating Formulaicness in the Automatic Evaluation of Naturalness: A Case Study in Logic-to-Text Generation](https://aclanthology.org/2025.inlg-main.21/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference*
19. [Insight discovery in structured data](https://aclanthology.org/2025.ynlg-main.4/) *— 2025 · Proceedings of the 1st Workshop for Young Researchers in Natural Language Generation*
20. [LoRMA: Low-Rank Multiplicative Adaptation for LLMs](https://aclanthology.org/2025.findings-acl.527/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
21. [Multilingual Verbalisation of Knowledge Graphs](https://aclanthology.org/2025.findings-emnlp.60/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
22. [On Reducing Factual Hallucinations in Graph-to-Text Generation Using Large Language Models](https://aclanthology.org/2025.genaik-1.5/) *— 2025 · Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)*
23. [PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models](https://aclanthology.org/2025.coling-main.585/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
24. [PrAd: Prompt Adaptive Tuning for Decoder-only Language Models](https://aclanthology.org/2025.findings-emnlp.254/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
25. [Scaling Up Data-to-Text Generation to Longer Sequences: A New Dataset and Benchmark Results for Generation from Large Triple Sets](https://aclanthology.org/2025.inlg-main.47/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference*
26. [Semantic Evaluation of Multilingual Data-to-Text Generation via NLI Fine-Tuning: Precision, Recall and F1 scores](https://aclanthology.org/2025.findings-acl.542/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
27. [Standard Quality Criteria Derived from Current NLP Evaluations for Guiding Evaluation Design and Grounding Comparability and AI Compliance Assessments](https://aclanthology.org/2025.findings-acl.1370/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
28. [StructFact: Reasoning Factual Knowledge from Structured Data with Large Language Models](https://aclanthology.org/2025.findings-acl.391/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
29. [Superpose Task-specific Features for Model Merging](https://aclanthology.org/2025.emnlp-main.210/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
30. [The 2024 GEM Shared Task on Multilingual Data-to-Text Generation: English and Spanish Qualitative Evaluation Results](https://aclanthology.org/2025.inlg-genchal.1/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference: Generation Challenges*
31. [WebNLG-IT: Construction of an aligned RDF-Italian corpus through Machine Translation techniques](https://aclanthology.org/2025.findings-acl.625/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
32. [\textit D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Models](https://aclanthology.org/2025.findings-acl.174/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
33. [AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning](https://aclanthology.org/2024.naacl-long.282/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
34. [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://aclanthology.org/2024.acl-long.651/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
35. [Bi-Directional Multi-Granularity Generation Framework for Knowledge Graph-to-Text with Large Language Model](https://aclanthology.org/2024.acl-short.14/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
36. [DCU-ADAPT-modPB at the GEM”24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation](https://aclanthology.org/2024.inlg-genchal.7/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges*
37. [Demonstration Selection Strategies for Numerical Time Series Data-to-Text](https://aclanthology.org/2024.findings-emnlp.435/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
38. [Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics](https://aclanthology.org/2024.lrec-main.423/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
39. [Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering](https://aclanthology.org/2024.emnlp-main.594/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
40. [Exploring Data Augmentation in Neural DRS-to-Text Generation](https://aclanthology.org/2024.eacl-long.132/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Exploring the Synergy of Dual-path Encoder and Alignment Module for Better Graph-to-Text Generation](https://aclanthology.org/2024.lrec-main.612/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
42. [Exploring the impact of data representation on neural data-to-text generation](https://aclanthology.org/2024.inlg-main.20/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference*
43. [FIRE: A Dataset for Financial Relation Extraction](https://aclanthology.org/2024.findings-naacl.230/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
44. [Filling Gaps in Wikipedia: Leveraging Data-to-Text Generation to Improve Encyclopedic Coverage of Underrepresented Groups](https://aclanthology.org/2024.inlg-demos.6/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference: System Demonstrations*
45. [Fine-grained Hallucination Detection and Editing for Language Models](https://arxiv.org/abs/2401.06855) *— 2024 · arXiv:2401.06855*
46. [GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network](https://aclanthology.org/2024.findings-acl.237/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
47. [GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism](https://aclanthology.org/2024.findings-naacl.58/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
48. [Graph Language Models](https://aclanthology.org/2024.acl-long.245/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Intrinsic Task-based Evaluation for Referring Expression Generation](https://aclanthology.org/2024.acl-long.389/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
50. [Large Language Models for Scientific Information Extraction: An Empirical Study for Virology](https://aclanthology.org/2024.findings-eacl.26/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
51. [LoRA Meets Dropout under a Unified Framework](https://aclanthology.org/2024.findings-acl.119/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
52. [MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning](https://aclanthology.org/2024.acl-long.380/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs](https://aclanthology.org/2024.findings-acl.400/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
54. [Pipeline Neural Data-to-text with Large Language Models](https://aclanthology.org/2024.inlg-main.27/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference*
55. [Probing Omissions and Distortions in Transformer-based RDF-to-Text Models](https://arxiv.org/abs/2409.16707) *— 2024 · arXiv:2409.16707*
56. [Prompting for Numerical Sequences: A Case Study on Market Comment Generation](https://aclanthology.org/2024.lrec-main.1155/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
57. [ReproHum \#1018-09: Reproducing Human Evaluations of Redundancy Errors in Data-To-Text Systems](https://aclanthology.org/2024.humeval-1.16/) *— 2024 · Proceedings of the Fourth Workshop on Human Evaluation of NLP Systems (HumEval) @ LREC-COLING 2024*
58. [SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation](https://aclanthology.org/2024.acl-long.36/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://aclanthology.org/2024.findings-naacl.106/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
60. [Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks](https://aclanthology.org/2024.findings-emnlp.688/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
61. [A Sequence-to-Sequence&Set Model for Text-to-Table Generation](https://aclanthology.org/2023.findings-acl.330/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
62. [ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation](https://aclanthology.org/2023.findings-emnlp.229/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
63. [AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets](https://aclanthology.org/2023.findings-acl.670/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
64. [Better Translation + Split and Generate for Multilingual RDF-to-Text (WebNLG 2023)](https://aclanthology.org/2023.mmnlg-1.8/) *— 2023 · Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)*
65. [CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality](https://aclanthology.org/2023.acl-long.168/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
66. [CURED4NLG: A Dataset for Table-to-Text Generation](https://aclanthology.org/2023.ldk-1.39/) *— 2023 · Proceedings of the 4th Conference on Language, Data and Knowledge*
67. [DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models](https://aclanthology.org/2023.acl-long.456/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
68. [Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions](https://arxiv.org/abs/2308.00425) *— 2023 · arXiv:2308.00425*
69. [DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation](https://aclanthology.org/2023.eacl-main.239/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
70. [Evaluating Generative Models for Graph-to-Text Generation](https://aclanthology.org/2023.ranlp-1.133/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
71. [FactKG: Fact Verification via Reasoning on Knowledge Graphs](https://aclanthology.org/2023.acl-long.895/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
72. [Faithful Low-Resource Data-to-Text Generation through Cycle Training](https://aclanthology.org/2023.acl-long.160/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
73. [Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning](https://aclanthology.org/2023.acl-long.894/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://aclanthology.org/2023.acl-long.522/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
75. [Generating and Answering Simple and Complex Questions from Text and from Knowledge Graphs](https://aclanthology.org/2023.ijcnlp-main.19/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
76. [Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs](https://aclanthology.org/2023.emnlp-main.770/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
77. [INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback](https://aclanthology.org/2023.emnlp-main.365/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
78. [Is GPT-4 a Good Data Analyst?](https://aclanthology.org/2023.findings-emnlp.637/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
79. [Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation](https://aclanthology.org/2023.starsem-1.17/) *— 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)*
80. [Knowledge-Grounded Natural Language Recommendation Explanation](https://aclanthology.org/2023.blackboxnlp-1.1/) *— 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
81. [LexExMachinaQA: A framework for the automatic induction ofontology lexica for Question Answering over Linked Data](https://aclanthology.org/2023.ldk-1.18/) *— 2023 · Proceedings of the 4th Conference on Language, Data and Knowledge*
82. [MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types](https://aclanthology.org/2023.findings-acl.274/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
83. [MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation](https://aclanthology.org/2023.findings-acl.704/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
84. [Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models](https://aclanthology.org/2023.eacl-main.176/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
85. [Phylogeny-Inspired Soft Prompts For Data-to-Text Generation in Low-Resource Languages](https://aclanthology.org/2023.ijcnlp-main.13/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
86. [Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models](https://aclanthology.org/2023.ijcnlp-main.69/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes](https://aclanthology.org/2023.acl-long.283/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction](https://aclanthology.org/2023.emnlp-main.537/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
89. [TaTA: A Multilingual Table-to-Text Dataset for African Languages](https://aclanthology.org/2023.findings-emnlp.118/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
90. [TabGenie: A Toolkit for Table-to-Text Generation](https://aclanthology.org/2023.acl-demo.42/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
91. [Text Fact Transfer](https://aclanthology.org/2023.emnlp-main.288/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
92. [The 2023 WebNLG Shared Task on Low Resource Languages. Overview and Evaluation Results (WebNLG 2023)](https://aclanthology.org/2023.mmnlg-1.6/) *— 2023 · Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)*
93. [The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding](https://aclanthology.org/2023.findings-acl.351/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
94. [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://arxiv.org/abs/2306.08302) *— 2023 · arXiv:2306.08302 · IEEE Transactions on Knowledge and Data Engineering (TKDE) 2024*
95. [Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model](https://aclanthology.org/2023.acl-long.281/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
96. [Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs](https://aclanthology.org/2023.mmnlg-1.5/) *— 2023 · Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)*
97. [Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining](https://aclanthology.org/2023.emnlp-main.184/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
98. [XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages](https://aclanthology.org/2023.inlg-main.2/) *— 2023 · Proceedings of the 16th International Natural Language Generation Conference*
99. [AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning](https://aclanthology.org/2022.emnlp-main.388/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
100. [AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models](https://aclanthology.org/2022.findings-emnlp.240/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*

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