{"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/graph-evolving-meta-learning-for-low-resource","title":"Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation","arxiv_id":"2012.11988","date":"2020-12-22","proceeding":null,"authors":["Shuai Lin","Pan Zhou","Xiaodan Liang","Jianheng Tang","Ruihui Zhao","Ziliang Chen","Liang Lin"],"abstract":"Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. It is capitalized on a commonsense knowledge graph to characterize the prior disease-symptom relations. Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues. More importantly, by dynamically evolving disease-symptom graphs, GEML also well addresses the real-world challenges that the disease-symptom correlations of each disease may vary or evolve along with more diagnostic cases. Extensive experiment results on the CMDD dataset and our newly-collected Chunyu dataset testify the superiority of our approach over state-of-the-art approaches. Besides, our GEML can generate an enriched dialogue-sensitive knowledge graph in an online manner, which could benefit other tasks grounded on knowledge graph.","url_abs":"https://arxiv.org/abs/2012.11988v1","url_pdf":"https://arxiv.org/pdf/2012.11988v1.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":"graph-evolving-meta-learning-for-low-resource","repo_url":"https://github.com/ha-lins/GEML-MDG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"dialogue-generation","task_name":"Dialogue Generation"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2012.11988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.11988"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ha-lins/GEML-MDG","reach":null}],"summary":{"ran_violates":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"f0c0974ae7efc437","entry":"get_location","repo":"ha-lins/GEML-MDG","repo_kind":"official","path":"CY_DataReadandMetric.py","file_url":"https://github.com/ha-lins/GEML-MDG/blob/HEAD/CY_DataReadandMetric.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f0c0974ae7efc437"}},{"code_sha256_prefix":"408a9e2a7d06d624","entry":"get_xingzhi","repo":"ha-lins/GEML-MDG","repo_kind":"official","path":"CY_DataReadandMetric.py","file_url":"https://github.com/ha-lins/GEML-MDG/blob/HEAD/CY_DataReadandMetric.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"408a9e2a7d06d624"}},{"code_sha256_prefix":"39f0ddbb4398e59f","entry":"get_youyin","repo":"ha-lins/GEML-MDG","repo_kind":"official","path":"CY_DataReadandMetric.py","file_url":"https://github.com/ha-lins/GEML-MDG/blob/HEAD/CY_DataReadandMetric.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"39f0ddbb4398e59f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}