{"url":"/dataset/e2e","name":"E2E","full_name":"End-to-End NLG Challenge","description_markdown":"End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena.\r\n\r\nSource: [Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge](/paper/evaluating-the-state-of-the-art-of-end-to-end)","description_withheld":null,"homepage":"http://www.macs.hw.ac.uk/InteractionLab/E2E/","introduced_date":"2017-06-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-e2e-dataset-new-challenges-for-end-to-end","title":"The E2E Dataset: New Challenges For End-to-End Generation","first_author":"Jekaterina Novikova","url":null},"license":{"name":"CC BY-SA 4.0","url":"https://creativecommons.org/licenses/by-sa/4.0/"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Text Generation","url":"/task/text-generation","datasets_with_task":"/datasets/task/text-generation"},{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"},{"name":"Data-to-Text Generation","url":"/task/data-to-text-generation","datasets_with_task":"/datasets/task/data-to-text-generation"},{"name":"Table-to-Text Generation","url":"/task/table-to-text-generation","datasets_with_task":"/datasets/task/table-to-text-generation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["E2E NLG Challenge","Cleaned E2E NLG Challenge","E2E"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/tuetschek/e2e_nlg","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/e2e_nlg","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/e2e_cleaned","frameworks":["tf","jax"]}],"num_papers_in_archive":90,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/data-to-text-generation-on-e2e-nlg-challenge","task":"Data-to-Text Generation","dataset_variant":"E2E NLG Challenge","rows":11,"metrics":["BLEU","METEOR","NIST","ROUGE-L","CIDEr","Number of parameters (M)"],"first_row_in_archive_order":{"model":"S_1^R","paper":"/paper/pragmatically-informative-text-generation","metrics":{"BLEU":"68.60","CIDEr":"2.37","METEOR":"45.25","NIST":"8.73","ROUGE-L":"70.82"},"code_links":[{"title":"sIncerass/prag_generation","url":"https://github.com/sIncerass/prag_generation"},{"title":"reallygooday/60daysofudacity","url":"https://github.com/reallygooday/60daysofudacity"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/data-to-text-generation-on-cleaned-e2e-nlg-1","task":"Data-to-Text Generation","dataset_variant":"Cleaned E2E NLG Challenge","rows":7,"metrics":["BLEU (Test set)","METEOR (Validation set)"],"first_row_in_archive_order":{"model":"Control Prefixes (T5-large)","paper":"/paper/control-prefixes-for-text-generation","metrics":{"BLEU (Test set)":"44.15"},"code_links":[{"title":"Yale-LILY/dart","url":"https://github.com/Yale-LILY/dart"},{"title":"jordiclive/ControlPrefixes","url":"https://github.com/jordiclive/ControlPrefixes"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/data-to-text-generation-on-e2e","task":"Data-to-Text Generation","dataset_variant":"E2E","rows":2,"metrics":["METEOR"],"first_row_in_archive_order":{"model":"self-mem + new data (random)","paper":"/paper/self-training-from-self-memory-in-data-to","metrics":{"METEOR":"46.11"},"code_links":[{"title":"hoangthangta/stsm","url":"https://github.com/hoangthangta/stsm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/table-to-text-generation-on-e2e","task":"Table-to-Text Generation","dataset_variant":"E2E","rows":2,"metrics":["BLEU","CIDEr","METEOR","NIST","ROUGE-L"],"first_row_in_archive_order":{"model":"HTLM (fine-tuning)","paper":"/paper/htlm-hyper-text-pre-training-and-prompting-of","metrics":{"BLEU":"70.3","CIDEr":"2.47","METEOR":"46.3","NIST":"8.90","ROUGE-L":"70.8"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/tricy-trigger-guided-data-to-text-generation-1","title":"TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy","date":"2024-01-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/self-training-from-self-memory-in-data-to","title":"Self-training from Self-memory in Data-to-text Generation","date":"2024-01-19","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/control-prefixes-for-text-generation","title":"Control Prefixes for Parameter-Efficient Text Generation","date":"2021-10-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/htlm-hyper-text-pre-training-and-prompting-of","title":"HTLM: Hyper-Text Pre-Training and Prompting of Language Models","date":"2021-07-14","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/the-gem-benchmark-natural-language-generation","title":"The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics","date":"2021-02-02","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/have-your-text-and-use-it-too-end-to-end","title":"Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity","date":"2020-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semantic-noise-matters-for-neural-natural","title":"Semantic Noise Matters for Neural Natural Language Generation","date":"2019-11-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/copy-mechanism-and-tailored-training-for","title":"Copy mechanism and tailored training for character-based data-to-text generation","date":"2019-04-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pragmatically-informative-text-generation","title":"Pragmatically Informative Text Generation","date":"2019-04-02","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/e2e-nlg-challenge-neural-models-vs-templates","title":"E2E NLG Challenge: Neural Models vs. Templates","date":"2018-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/findings-of-the-e2e-nlg-challenge","title":"Findings of the E2E NLG Challenge","date":"2018-10-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-deep-ensemble-model-with-slot-alignment-for","title":"A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation","date":"2018-05-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/tnt-nlg-system-1-using-a-statistical-nlg-to","title":"TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation","date":"2018-04-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/attention-regularized-sequence-to-sequence","title":"Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge","date":"2018-03-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/technical-report-for-e2e-nlg-challenge","title":"Technical Report for E2E NLG Challenge","date":"2017-12-19","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}