{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CAWIBM3GEPNL5S6PRIEUFOVPY3","short_pith_number":"pith:CAWIBM3G","schema_version":"1.0","canonical_sha256":"102c80b36623dabecbcf8a0942baafc6faa95e00b4101e32c82ad8f1671890d7","source":{"kind":"arxiv","id":"1910.00610","version":1},"attestation_state":"computed","paper":{"title":"DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hung-yi Lee, Yi-Lin Tuan, Yun-Nung Chen","submitted_at":"2019-10-01T18:29:08Z","abstract_excerpt":"Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Our new task and corpus aids in understanding the influence of dynamic knowledge graphs on responses generation. Also, we propose a preliminary model that selects an output from two networks a"},"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":"1910.00610","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-01T18:29:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6e2ad6e38aa14ce78e74cc9813074c890646482dd6012965408ea3bae7c3c84d","abstract_canon_sha256":"68ed423af10a1fe496a724e83a8fb5cdc9cfa53cc1b7ea9ceb366a37a9110b4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:09:17.472329Z","signature_b64":"9GqlQKr1RtdM2CGd+/b1czQGzMU/BBJFypCyCMWR2L3my2jsVfT3QvavWchpKmIv4Ca+263KtkV823xApY5+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"102c80b36623dabecbcf8a0942baafc6faa95e00b4101e32c82ad8f1671890d7","last_reissued_at":"2026-07-05T00:09:17.471960Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:09:17.471960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hung-yi Lee, Yi-Lin Tuan, Yun-Nung Chen","submitted_at":"2019-10-01T18:29:08Z","abstract_excerpt":"Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Our new task and corpus aids in understanding the influence of dynamic knowledge graphs on responses generation. Also, we propose a preliminary model that selects an output from two networks a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.00610","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/1910.00610/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":"1910.00610","created_at":"2026-07-05T00:09:17.472018+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.00610v1","created_at":"2026-07-05T00:09:17.472018+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.00610","created_at":"2026-07-05T00:09:17.472018+00:00"},{"alias_kind":"pith_short_12","alias_value":"CAWIBM3GEPNL","created_at":"2026-07-05T00:09:17.472018+00:00"},{"alias_kind":"pith_short_16","alias_value":"CAWIBM3GEPNL5S6P","created_at":"2026-07-05T00:09:17.472018+00:00"},{"alias_kind":"pith_short_8","alias_value":"CAWIBM3G","created_at":"2026-07-05T00:09:17.472018+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.10877","citing_title":"Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3","json":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3.json","graph_json":"https://pith.science/api/pith-number/CAWIBM3GEPNL5S6PRIEUFOVPY3/graph.json","events_json":"https://pith.science/api/pith-number/CAWIBM3GEPNL5S6PRIEUFOVPY3/events.json","paper":"https://pith.science/paper/CAWIBM3G"},"agent_actions":{"view_html":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3","download_json":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3.json","view_paper":"https://pith.science/paper/CAWIBM3G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.00610&json=true","fetch_graph":"https://pith.science/api/pith-number/CAWIBM3GEPNL5S6PRIEUFOVPY3/graph.json","fetch_events":"https://pith.science/api/pith-number/CAWIBM3GEPNL5S6PRIEUFOVPY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3/action/storage_attestation","attest_author":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3/action/author_attestation","sign_citation":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3/action/citation_signature","submit_replication":"https://pith.science/pith/CAWIBM3GEPNL5S6PRIEUFOVPY3/action/replication_record"}},"created_at":"2026-07-05T00:09:17.472018+00:00","updated_at":"2026-07-05T00:09:17.472018+00:00"}