{"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/attention-regularized-sequence-to-sequence","title":"Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge","arxiv_id":null,"date":"2018-03-01","proceeding":"E2E NLG Challenge System Descriptions 2018 3","authors":["Biao Zhang","Jing Yang","Qian Lin","Jinsong Su"],"abstract":"This paper describes our system used for the end-to-end (E2E) natural language generation (NLG) challenge. The challenge collects a novel dataset for spoken dialogue system in the restaurant domain, which shows more lexical richness and syntactic variation and requires content selection (Novikova et al., 2017). To solve this challenge, we employ the CAEncoder-enhanced sequence-tosequence learning model (Zhang et al., 2017) and propose an attention regularizer to spread attention weights across input words as well as control the overfitting problem. Without any specific designation, our system yields very promising performance. Particularly, our system achieves a ROUGE-L score of 0.7083, the best result among all submitted primary systems.","url_abs":"http://www.macs.hw.ac.uk/InteractionLab/E2E/final_papers/E2E-Zhang.pdf","url_pdf":"http://www.macs.hw.ac.uk/InteractionLab/E2E/final_papers/E2E-Zhang.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":"Zhang","rank_in_archive_order":8,"of":11,"metrics":{"BLEU":"65.45","CIDEr":"2.1012","METEOR":"43.92","NIST":"8.1804","ROUGE-L":"70.83"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}