--- title: "Who cites Text-to-Text Pre-Training for Data-to-Text Tasks" description: "110 papers of the Elamur Research library cite «Text-to-Text Pre-Training for Data-to-Text Tasks», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/kale-2020-text-text-pre-training-data/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kale-2020-text-text-pre-training-data/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Text-to-Text Pre-Training for Data-to-Text Tasks](https://md.research.elamur.ai/free/en/papers/kale-2020-text-text-pre-training-data/) / cited by # Who cites «Text-to-Text Pre-Training for Data-to-Text Tasks» 110 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Text-to-Text Pre-Training for Data-to-Text Tasks»](https://md.research.elamur.ai/free/en/papers/kale-2020-text-text-pre-training-data/) 1. **[mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 3,226 citations* 2. **[Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,556 citations* 3. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,551 citations* 4. **[UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 407 citations* 5. **[Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Oshin Agarwal, Heming Ge, Siamak Shakeri, et al.*2021 · 239 citations* 6. [**DART: Open-Domain Structured Data Record to Text Generation arXiv:2007.02871**Linyong Nan, Dragomir Radev, Rui Zhang, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/) 7. **[Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/) [Workshop on Natural Language Processing for Conversational AI](https://md.research.elamur.ai/free/en/venues/nlp4convai/)**Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, et al.*2021 · 151 citations* 8. **[IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://md.research.elamur.ai/free/en/papers/dabre-2022-indicbart-pre-trained-model-indic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Raj Dabre, Himani Shrotriya, Anoop Kunchukuttan, et al.*2022 · 141 citations* 9. **[Understanding Transformer Memorization Recall Through Idioms](https://md.research.elamur.ai/free/en/papers/haviv-2023-understanding-transformer-memorization-recall-through/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adi Haviv, Ido Cohen, Jacob Gidron, et al.*2023 · 62 citations* 10. **[The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://md.research.elamur.ai/free/en/papers/loakman-2023-iron-ic-melting-pot-reviewing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tyler Loakman, Aaron Maladry, Chenghua Lin*2023 · 19 citations* 11. **[ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/straka-2023-fal-corpipe-crac-2023-larger/) [Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC)](https://md.research.elamur.ai/free/en/venues/crac/)**Milan Straka*2023 · 17 citations* 12. **[DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification](https://md.research.elamur.ai/free/en/papers/anuranjana-2023-discoflan-instruction-fine-tuning-refined/) [Shared Task on Discourse Relation Parsing and Treebanking](https://md.research.elamur.ai/free/en/venues/disrpt/)**Kaveri Anuranjana*2023 · 13 citations* 13. **[Improving Cross-lingual Transfer through Subtree-aware Word Reordering](https://md.research.elamur.ai/free/en/papers/arviv-2023-improving-cross-lingual-transfer-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, et al.*2023 · 4 citations* 97 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kale-2020-text-text-pre-training-data/) — Text-to-Text Pre-Training for Data-to-Text Tasks