{"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/automatic-stance-detection-using-end-to-end","title":"Automatic Stance Detection Using End-to-End Memory Networks","arxiv_id":"1804.07581","date":"2018-04-20","proceeding":"NAACL 2018 6","authors":["Mitra Mohtarami","Ramy Baly","James Glass","Preslav Nakov","Lluis Marquez","Alessandro Moschitti"],"abstract":"We present a novel end-to-end memory network for stance detection, which\njointly (i) predicts whether a document agrees, disagrees, discusses or is\nunrelated with respect to a given target claim, and also (ii) extracts snippets\nof evidence for that prediction. The network operates at the paragraph level\nand integrates convolutional and recurrent neural networks, as well as a\nsimilarity matrix as part of the overall architecture. The experimental\nevaluation on the Fake News Challenge dataset shows state-of-the-art\nperformance.","url_abs":"http://arxiv.org/abs/1804.07581v1","url_pdf":"http://arxiv.org/pdf/1804.07581v1.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":"stance-detection","task_name":"Stance Detection"}],"methods":[{"method_slug":"end-to-end-memory-network","method_name":"End-To-End Memory Network"},{"method_slug":"memory-network","method_name":"Memory Network"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fake-news-detection-on-fnc-1","task":"Fake News Detection","dataset":"FNC-1","model":"Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018)","rank_in_archive_order":6,"of":10,"metrics":{"Weighted Accuracy":"81.23"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-fnc-1","task":"Fake News Detection","dataset":"FNC-1","model":"Neural method from Mohtarami et al. (Mohtarami et al., 2018)","rank_in_archive_order":7,"of":10,"metrics":{"Weighted Accuracy":"78.97"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.07581","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}