{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:RBXZZZ2JQDOC5N6HYBUHYUTWRJ","short_pith_number":"pith:RBXZZZ2J","schema_version":"1.0","canonical_sha256":"886f9ce74980dc2eb7c7c0687c52768a7b95597633d3a7f3d6b685c56e2165e5","source":{"kind":"arxiv","id":"2205.14727","version":1},"attestation_state":"computed","paper":{"title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.MM"],"primary_cat":"cs.CL","authors_text":"Jianxin Pang, Minlie Huang, Qianfeng Tie, Weiquan Fan, Wenjing Han, Xiangmin Xu, Xiaofen Xing, Yirong Chen","submitted_at":"2022-05-29T17:45:12Z","abstract_excerpt":"Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the proce"},"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":"2205.14727","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-29T17:45:12Z","cross_cats_sorted":["cs.AI","cs.HC","cs.MM"],"title_canon_sha256":"6192b6237a04534203c23a795e29f2d027350b59b82b62ff2450361b873f59a9","abstract_canon_sha256":"2a4b371bef66a0e8a057b334673bd4f723176662087e6e2962ad06a7c8158e41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:20.787844Z","signature_b64":"A7ZU6XDH8qI/z7rdqrySXZoEjbij8rxmjC4O092qeOO/CHz/8NWbbln/N+D9MoXxuQBAr0V25AYwm7vu2j0rDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"886f9ce74980dc2eb7c7c0687c52768a7b95597633d3a7f3d6b685c56e2165e5","last_reissued_at":"2026-07-05T04:27:20.787366Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:20.787366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.MM"],"primary_cat":"cs.CL","authors_text":"Jianxin Pang, Minlie Huang, Qianfeng Tie, Weiquan Fan, Wenjing Han, Xiangmin Xu, Xiaofen Xing, Yirong Chen","submitted_at":"2022-05-29T17:45:12Z","abstract_excerpt":"Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the proce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14727","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/2205.14727/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":"2205.14727","created_at":"2026-07-05T04:27:20.787420+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14727v1","created_at":"2026-07-05T04:27:20.787420+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14727","created_at":"2026-07-05T04:27:20.787420+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBXZZZ2JQDOC","created_at":"2026-07-05T04:27:20.787420+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBXZZZ2JQDOC5N6H","created_at":"2026-07-05T04:27:20.787420+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBXZZZ2J","created_at":"2026-07-05T04:27:20.787420+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04591","citing_title":"Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues","ref_index":89,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20603","citing_title":"A Survey of Large Language Models for Perception and Measurement of Human Psychology","ref_index":64,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ","json":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ.json","graph_json":"https://pith.science/api/pith-number/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/graph.json","events_json":"https://pith.science/api/pith-number/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/events.json","paper":"https://pith.science/paper/RBXZZZ2J"},"agent_actions":{"view_html":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ","download_json":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ.json","view_paper":"https://pith.science/paper/RBXZZZ2J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14727&json=true","fetch_graph":"https://pith.science/api/pith-number/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/action/storage_attestation","attest_author":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/action/author_attestation","sign_citation":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/action/citation_signature","submit_replication":"https://pith.science/pith/RBXZZZ2JQDOC5N6HYBUHYUTWRJ/action/replication_record"}},"created_at":"2026-07-05T04:27:20.787420+00:00","updated_at":"2026-07-05T04:27:20.787420+00:00"}