{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:C5NTBNA3Q5OBCYJGAV4ZIX7BGH","short_pith_number":"pith:C5NTBNA3","schema_version":"1.0","canonical_sha256":"175b30b41b875c1161260579945fe131ecf1ba538fe1a525e85c775f879e0a14","source":{"kind":"arxiv","id":"2212.01739","version":1},"attestation_state":"computed","paper":{"title":"KPT: Keyword-guided Pre-training for Grounded Dialog Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fei Mi, Minlie Huang, Qi Zhu, Qun Liu, Xiaoyan Zhu, Xin Jiang, Yasheng Wang, Yitong Li, Zheng Zhang","submitted_at":"2022-12-04T04:05:01Z","abstract_excerpt":"Incorporating external knowledge into the response generation process is essential to building more helpful and reliable dialog agents. However, collecting knowledge-grounded conversations is often costly, calling for a better pre-trained model for grounded dialog generation that generalizes well w.r.t. different types of knowledge. In this work, we propose KPT (Keyword-guided Pre-Training), a novel self-supervised pre-training method for grounded dialog generation without relying on extra knowledge annotation. Specifically, we use a pre-trained language model to extract the most uncertain tok"},"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":"2212.01739","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-04T04:05:01Z","cross_cats_sorted":[],"title_canon_sha256":"0e0ee8e6c39082cbf4708fe42321881477e5eceb0399043c1c943d8cb29c020c","abstract_canon_sha256":"4de4b7bbda184ab775f8590a700f9c90dcc6f9ae3502e3ca72d123fed4407fda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:22:16.068958Z","signature_b64":"vto2Y4SPcRxxKnQCpOpPv/cuPBDZpVFkfj7ozrpBbBi5kBTV08HPBpsfD8/gzeWAkoabipoTWzhaCoMdhBqAAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"175b30b41b875c1161260579945fe131ecf1ba538fe1a525e85c775f879e0a14","last_reissued_at":"2026-07-05T05:22:16.068516Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:22:16.068516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KPT: Keyword-guided Pre-training for Grounded Dialog Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fei Mi, Minlie Huang, Qi Zhu, Qun Liu, Xiaoyan Zhu, Xin Jiang, Yasheng Wang, Yitong Li, Zheng Zhang","submitted_at":"2022-12-04T04:05:01Z","abstract_excerpt":"Incorporating external knowledge into the response generation process is essential to building more helpful and reliable dialog agents. However, collecting knowledge-grounded conversations is often costly, calling for a better pre-trained model for grounded dialog generation that generalizes well w.r.t. different types of knowledge. In this work, we propose KPT (Keyword-guided Pre-Training), a novel self-supervised pre-training method for grounded dialog generation without relying on extra knowledge annotation. Specifically, we use a pre-trained language model to extract the most uncertain tok"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01739","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/2212.01739/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":"2212.01739","created_at":"2026-07-05T05:22:16.068578+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.01739v1","created_at":"2026-07-05T05:22:16.068578+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01739","created_at":"2026-07-05T05:22:16.068578+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5NTBNA3Q5OB","created_at":"2026-07-05T05:22:16.068578+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5NTBNA3Q5OBCYJG","created_at":"2026-07-05T05:22:16.068578+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5NTBNA3","created_at":"2026-07-05T05:22:16.068578+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH","json":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH.json","graph_json":"https://pith.science/api/pith-number/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/graph.json","events_json":"https://pith.science/api/pith-number/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/events.json","paper":"https://pith.science/paper/C5NTBNA3"},"agent_actions":{"view_html":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH","download_json":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH.json","view_paper":"https://pith.science/paper/C5NTBNA3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.01739&json=true","fetch_graph":"https://pith.science/api/pith-number/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/graph.json","fetch_events":"https://pith.science/api/pith-number/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/action/storage_attestation","attest_author":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/action/author_attestation","sign_citation":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/action/citation_signature","submit_replication":"https://pith.science/pith/C5NTBNA3Q5OBCYJGAV4ZIX7BGH/action/replication_record"}},"created_at":"2026-07-05T05:22:16.068578+00:00","updated_at":"2026-07-05T05:22:16.068578+00:00"}