{"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/densely-connected-attention-propagation-for","title":"Densely Connected Attention Propagation for Reading Comprehension","arxiv_id":"1811.04210","date":"2018-11-10","proceeding":"NeurIPS 2018 12","authors":["Yi Tay","Luu Anh Tuan","Siu Cheung Hui","Jian Su"],"abstract":"We propose DecaProp (Densely Connected Attention Propagation), a new densely\nconnected neural architecture for reading comprehension (RC). There are two\ndistinct characteristics of our model. Firstly, our model densely connects all\npairwise layers of the network, modeling relationships between passage and\nquery across all hierarchical levels. Secondly, the dense connectors in our\nnetwork are learned via attention instead of standard residual skip-connectors.\nTo this end, we propose novel Bidirectional Attention Connectors (BAC) for\nefficiently forging connections throughout the network. We conduct extensive\nexperiments on four challenging RC benchmarks. Our proposed approach achieves\nstate-of-the-art results on all four, outperforming existing baselines by up to\n$2.6\\%-14.2\\%$ in absolute F1 score.","url_abs":"http://arxiv.org/abs/1811.04210v2","url_pdf":"http://arxiv.org/pdf/1811.04210v2.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":"densely-connected-attention-propagation-for","repo_url":"https://github.com/ajenningsfrankston/NIPS2018_DECAPROP-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"densely-connected-attention-propagation-for","repo_url":"https://github.com/vanzytay/NIPS2018_DECAPROP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"open-domain-question-answering","task_name":"Open-Domain Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/open-domain-question-answering-on-quasar","task":"Open-Domain Question Answering","dataset":"Quasar","model":"DecaProp","rank_in_archive_order":3,"of":6,"metrics":{"EM (Quasar-T)":"38.6","F1 (Quasar-T)":"46.9"},"uses_additional_data":false},{"leaderboard":"/sota/open-domain-question-answering-on-searchqa","task":"Open-Domain Question Answering","dataset":"SearchQA","model":"DECAPROP","rank_in_archive_order":5,"of":14,"metrics":{"EM":"62.2"},"uses_additional_data":false},{"leaderboard":"/sota/open-domain-question-answering-on-searchqa","task":"Open-Domain Question Answering","dataset":"SearchQA","model":"DecaProp","rank_in_archive_order":7,"of":14,"metrics":{"EM":"56.8","F1":"63.6","N-gram F1":"70.8","Unigram Acc":"62.2"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-narrativeqa","task":"Question Answering","dataset":"NarrativeQA","model":"DecaProp","rank_in_archive_order":5,"of":10,"metrics":{"BLEU-1":"44.35","BLEU-4":"27.61","METEOR":"21.80","Rouge-L":"44.69"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-newsqa","task":"Question Answering","dataset":"NewsQA","model":"DecaProp","rank_in_archive_order":12,"of":18,"metrics":{"EM":"53.1","F1":"66.3"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-quasart-t","task":"Question Answering","dataset":"Quasart-T","model":"DECAPROP","rank_in_archive_order":6,"of":7,"metrics":{"EM":"38.6"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.04210","atlas_url":"https://app.syntology.ai/?focus=1811.04210","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}