{"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/finding-remo-related-memory-object-a-simple","title":"Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning","arxiv_id":"1801.08459","date":"2018-01-25","proceeding":"ICLR 2018 1","authors":["Jihyung Moon","Hyochang Yang","Sungzoon Cho"],"abstract":"To solve the text-based question and answering task that requires relational\nreasoning, it is necessary to memorize a large amount of information and find\nout the question relevant information from the memory. Most approaches were\nbased on external memory and four components proposed by Memory Network. The\ndistinctive component among them was the way of finding the necessary\ninformation and it contributes to the performance. Recently, a simple but\npowerful neural network module for reasoning called Relation Network (RN) has\nbeen introduced. We analyzed RN from the view of Memory Network, and realized\nthat its MLP component is able to reveal the complicate relation between\nquestion and object pair. Motivated from it, we introduce which uses MLP to\nfind out relevant information on Memory Network architecture. It shows new\nstate-of-the-art results in jointly trained bAbI-10k story-based question\nanswering tasks and bAbI dialog-based question answering tasks.","url_abs":"http://arxiv.org/abs/1801.08459v2","url_pdf":"http://arxiv.org/pdf/1801.08459v2.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":"question-answering","task_name":"Question Answering"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-network","task_name":"Relation Network"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-babi","task":"Question Answering","dataset":"bAbi","model":"ReMO","rank_in_archive_order":12,"of":14,"metrics":{"Mean Error Rate":" 1.2%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}