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In our model, a global memory encoder and a local memory decoder are\nproposed to share external knowledge. The encoder encodes dialogue history,\nmodifies global contextual representation, and generates a global memory\npointer. The decoder first generates a sketch response with unfilled slots.\nNext, it passes the global memory pointer to filter the external knowledge for\nrelevant information, then instantiates the slots via the local memory\npointers. We empirically show that our model can improve copy accuracy and\nmitigate the common out-of-vocabulary problem. As a result, GLMP is able to\nimprove over the previous state-of-the-art models in both simulated bAbI\nDialogue dataset and human-human Stanford Multi-domain Dialogue dataset on\nautomatic and human evaluation.","url_abs":"http://arxiv.org/abs/1901.04713v2","url_pdf":"http://arxiv.org/pdf/1901.04713v2.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":"global-to-local-memory-pointer-networks-for","repo_url":"https://github.com/jasonwu0731/GLMP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"global-to-local-memory-pointer-networks-for","repo_url":"https://github.com/LooperXX/DF-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"global-to-local-memory-pointer-networks-for","repo_url":"https://github.com/mangonihao/GLMP_Annotation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"global-to-local-memory-pointer-networks-for","repo_url":"https://github.com/scoyer/fg2seq","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"task-oriented-dialogue-systems","task_name":"Task-Oriented Dialogue Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/task-oriented-dialogue-systems-on-kvret","task":"Task-Oriented Dialogue Systems","dataset":"KVRET","model":"GLMP","rank_in_archive_order":4,"of":10,"metrics":{"BLEU":"14.79","Entity F1":"59.97"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.04713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.04713"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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