{"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/augmenting-memory-networks-for-rich-and","title":"Augmenting Memory Networks for Rich and Efficient Retrieval in Grounded Dialogue","arxiv_id":null,"date":"2021-11-16","proceeding":"ACL ARR November 2021 11","authors":["Anonymous"],"abstract":"Grounded dialogue consists of conditioning a conversation on additional latent inputs (\"factoids\") beyond the dialogue context, such as Wikipedia articles, IMDB reviews, persona, and images.  Due to a scarcity of <context, factoid> labels, it is common practice to jointly learn the knowledge-selection and grounded response generation tasks end-to-end. When conditioning the response on these factoids, previous work has either treated the factoids as a weighed average vector, or separately computed probabilities for each <context, factoid> pair. However, the former creates a bottleneck whilst the latter prevents factoids from being considered jointly. Our new method, PolyMemNet, learns a matrix representation of the context and factoids, allowing for multiple factoids to be jointly considered in response selection, without imposing a bottleneck. We show how this achieves up to a $17\\%$ boost in knowledge-selection accuracy and $13\\%$ in response-selection accuracy versus memory networks. ","url_abs":"https://openreview.net/forum?id=IezTlW7ERIm","url_pdf":"https://openreview.net/pdf?id=IezTlW7ERIm","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":"augmenting-memory-networks-for-rich-and","repo_url":"https://github.com/atticruckverwandlung/augmentingmemorynetworks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"response-generation","task_name":"Response Generation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}