{"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/open-question-answering-with-weakly","title":"Open Question Answering with Weakly Supervised Embedding Models","arxiv_id":"1404.4326","date":"2014-04-16","proceeding":null,"authors":["Antoine Bordes","Jason Weston","Nicolas Usunier"],"abstract":"Building computers able to answer questions on any subject is a long standing\ngoal of artificial intelligence. Promising progress has recently been achieved\nby methods that learn to map questions to logical forms or database queries.\nSuch approaches can be effective but at the cost of either large amounts of\nhuman-labeled data or by defining lexicons and grammars tailored by\npractitioners. In this paper, we instead take the radical approach of learning\nto map questions to vectorial feature representations. By mapping answers into\nthe same space one can query any knowledge base independent of its schema,\nwithout requiring any grammar or lexicon. Our method is trained with a new\noptimization procedure combining stochastic gradient descent followed by a\nfine-tuning step using the weak supervision provided by blending automatically\nand collaboratively generated resources. We empirically demonstrate that our\nmodel can capture meaningful signals from its noisy supervision leading to\nmajor improvements over paralex, the only existing method able to be trained on\nsimilar weakly labeled data.","url_abs":"http://arxiv.org/abs/1404.4326v1","url_pdf":"http://arxiv.org/pdf/1404.4326v1.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":"open-question","task_name":"Open-Ended Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-reverb","task":"Question Answering","dataset":"Reverb","model":"Weakly Supervised Embeddings","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"73%"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-webquestions","task":"Question Answering","dataset":"WebQuestions","model":"Weakly Supervised Embeddings","rank_in_archive_order":37,"of":37,"metrics":{"F1":"29.7%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1404.4326","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}