{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/findings-of-the-e2e-nlg-challenge","title":"Findings of the E2E NLG Challenge","arxiv_id":"1810.01170","date":"2018-10-02","proceeding":"WS 2018 11","authors":["Ondřej Dušek","Jekaterina Novikova","Verena Rieser"],"abstract":"This paper summarises the experimental setup and results of the first shared\ntask on end-to-end (E2E) natural language generation (NLG) in spoken dialogue\nsystems. Recent end-to-end generation systems are promising since they reduce\nthe need for data annotation. However, they are currently limited to small,\ndelexicalised datasets. The E2E NLG shared task aims to assess whether these\nnovel approaches can generate better-quality output by learning from a dataset\ncontaining higher lexical richness, syntactic complexity and diverse discourse\nphenomena. We compare 62 systems submitted by 17 institutions, covering a wide\nrange of approaches, including machine learning architectures -- with the\nmajority implementing sequence-to-sequence models (seq2seq) -- as well as\nsystems based on grammatical rules and templates.","url_abs":"http://arxiv.org/abs/1810.01170v1","url_pdf":"http://arxiv.org/pdf/1810.01170v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"findings-of-the-e2e-nlg-challenge","repo_url":"https://github.com/UFAL-DSG/tgen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"spoken-dialogue-systems","task_name":"Spoken Dialogue Systems"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/data-to-text-generation-on-e2e-nlg-challenge","task":"Data-to-Text Generation","dataset":"E2E NLG Challenge","model":"TGen","rank_in_archive_order":5,"of":11,"metrics":{"BLEU":"65.93","CIDEr":"2.2338","METEOR":"44.83","NIST":"8.6094","ROUGE-L":"68.50"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.01170","atlas_url":"https://app.syntology.ai/?focus=1810.01170","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}