{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UXQEKNJOC7JDWAJ24EY3BGCPAC","short_pith_number":"pith:UXQEKNJO","schema_version":"1.0","canonical_sha256":"a5e045352e17d23b013ae131b0984f00bee3631b3adc09765aeb32cea03ed4a8","source":{"kind":"arxiv","id":"2204.09867","version":1},"attestation_state":"computed","paper":{"title":"A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dacheng Tao, Meng Fang, Shuming Shi, Wei Bi, Yu Cao","submitted_at":"2022-04-21T03:49:54Z","abstract_excerpt":"Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue data at hand, it may be difficult to train a dialogue generation model well. We point out that the data challenges of this generation task lie in two aspects: first, it is expensive to scale up current persona-based dialogue datasets; second, each data sample in this task is more complex to learn with than conventional dialogue data. To alleviate the above data issues, we propose a data manipulation method, which is "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2204.09867","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-21T03:49:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b6ef6a2bf6a5de79193686627e521451def8c06e7c8865e0f7131f3347c2d04c","abstract_canon_sha256":"d56b7e0fcbb318ee46353fbd76a749b6b6d4155339b18a29462c5682743bbf58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:16:44.037978Z","signature_b64":"Qxy4h5b4sMP2gq+m8AtX8bfOf/bJDZduzfMY8rTqEXSi+BPr27PgyTF3N3yg37H+4Z02/5tc6+Hf+lVOV7wtBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5e045352e17d23b013ae131b0984f00bee3631b3adc09765aeb32cea03ed4a8","last_reissued_at":"2026-07-05T04:16:44.037565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:16:44.037565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dacheng Tao, Meng Fang, Shuming Shi, Wei Bi, Yu Cao","submitted_at":"2022-04-21T03:49:54Z","abstract_excerpt":"Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue data at hand, it may be difficult to train a dialogue generation model well. We point out that the data challenges of this generation task lie in two aspects: first, it is expensive to scale up current persona-based dialogue datasets; second, each data sample in this task is more complex to learn with than conventional dialogue data. To alleviate the above data issues, we propose a data manipulation method, which is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.09867","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2204.09867/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2204.09867","created_at":"2026-07-05T04:16:44.037629+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.09867v1","created_at":"2026-07-05T04:16:44.037629+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.09867","created_at":"2026-07-05T04:16:44.037629+00:00"},{"alias_kind":"pith_short_12","alias_value":"UXQEKNJOC7JD","created_at":"2026-07-05T04:16:44.037629+00:00"},{"alias_kind":"pith_short_16","alias_value":"UXQEKNJOC7JDWAJ2","created_at":"2026-07-05T04:16:44.037629+00:00"},{"alias_kind":"pith_short_8","alias_value":"UXQEKNJO","created_at":"2026-07-05T04:16:44.037629+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.21907","citing_title":"Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy","ref_index":78,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC","json":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC.json","graph_json":"https://pith.science/api/pith-number/UXQEKNJOC7JDWAJ24EY3BGCPAC/graph.json","events_json":"https://pith.science/api/pith-number/UXQEKNJOC7JDWAJ24EY3BGCPAC/events.json","paper":"https://pith.science/paper/UXQEKNJO"},"agent_actions":{"view_html":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC","download_json":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC.json","view_paper":"https://pith.science/paper/UXQEKNJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.09867&json=true","fetch_graph":"https://pith.science/api/pith-number/UXQEKNJOC7JDWAJ24EY3BGCPAC/graph.json","fetch_events":"https://pith.science/api/pith-number/UXQEKNJOC7JDWAJ24EY3BGCPAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC/action/storage_attestation","attest_author":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC/action/author_attestation","sign_citation":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC/action/citation_signature","submit_replication":"https://pith.science/pith/UXQEKNJOC7JDWAJ24EY3BGCPAC/action/replication_record"}},"created_at":"2026-07-05T04:16:44.037629+00:00","updated_at":"2026-07-05T04:16:44.037629+00:00"}