{"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/a-deep-ensemble-model-with-slot-alignment-for","title":"A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation","arxiv_id":"1805.06553","date":"2018-05-16","proceeding":"NAACL 2018 6","authors":["Juraj Juraska","Panagiotis Karagiannis","Kevin K. Bowden","Marilyn A. Walker"],"abstract":"Natural language generation lies at the core of generative dialogue systems\nand conversational agents. We describe an ensemble neural language generator,\nand present several novel methods for data representation and augmentation that\nyield improved results in our model. We test the model on three datasets in the\nrestaurant, TV and laptop domains, and report both objective and subjective\nevaluations of our best model. Using a range of automatic metrics, as well as\nhuman evaluators, we show that our approach achieves better results than\nstate-of-the-art models on the same datasets.","url_abs":"http://arxiv.org/abs/1805.06553v1","url_pdf":"http://arxiv.org/pdf/1805.06553v1.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":[],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"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":"Slug","rank_in_archive_order":4,"of":11,"metrics":{"BLEU":"66.19","METEOR":"44.54","NIST":"8.6130","ROUGE-L":"67.72"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.06553","atlas_url":"https://app.syntology.ai/?focus=1805.06553","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}