{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FPYU3GXUM5KZ7TR55MSOIBXMDA","short_pith_number":"pith:FPYU3GXU","schema_version":"1.0","canonical_sha256":"2bf14d9af467559fce3deb24e406ec1819e190e6664d7d653ff236dfe5cc0d0d","source":{"kind":"arxiv","id":"2104.08006","version":2},"attestation_state":"computed","paper":{"title":"ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bartuer Zhou, Biao Cheng, Bolun Yao, Can Xu, Daxin Jiang, Houqiang Li, Jiusheng Chen, Nan Duan, Ruofei Zhang, Weizhen Qi, Yeyun Gong, Yu Yan","submitted_at":"2021-04-16T10:00:43Z","abstract_excerpt":"Now, the pre-training technique is ubiquitous in natural language processing field. ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks. In this paper, we extend ProphetNet into other domains and languages, and present the ProphetNet family pre-training models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese generation model ProphetNet-Zh, two open-domain dialog generation models P"},"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":"2104.08006","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-04-16T10:00:43Z","cross_cats_sorted":[],"title_canon_sha256":"a479c677433350339bd8cc6d821921c0f245370f22ecf9aea98713cf69e983dd","abstract_canon_sha256":"34ffe2fcb1126154864596fe9800d46bc473ae42cf6d9271277d7a80281870fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:51:23.881473Z","signature_b64":"CbcWqzEkdwlVhSEF8sdHyEVBwuboQvGztM79EWtT5ZJOB9DOaQuStaGmM1S6or9VNMGAFDRfTHAy1oGXYoBjAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bf14d9af467559fce3deb24e406ec1819e190e6664d7d653ff236dfe5cc0d0d","last_reissued_at":"2026-07-05T02:51:23.881036Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:51:23.881036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bartuer Zhou, Biao Cheng, Bolun Yao, Can Xu, Daxin Jiang, Houqiang Li, Jiusheng Chen, Nan Duan, Ruofei Zhang, Weizhen Qi, Yeyun Gong, Yu Yan","submitted_at":"2021-04-16T10:00:43Z","abstract_excerpt":"Now, the pre-training technique is ubiquitous in natural language processing field. ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks. In this paper, we extend ProphetNet into other domains and languages, and present the ProphetNet family pre-training models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese generation model ProphetNet-Zh, two open-domain dialog generation models P"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.08006","kind":"arxiv","version":2},"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/2104.08006/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":"2104.08006","created_at":"2026-07-05T02:51:23.881090+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.08006v2","created_at":"2026-07-05T02:51:23.881090+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.08006","created_at":"2026-07-05T02:51:23.881090+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPYU3GXUM5KZ","created_at":"2026-07-05T02:51:23.881090+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPYU3GXUM5KZ7TR5","created_at":"2026-07-05T02:51:23.881090+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPYU3GXU","created_at":"2026-07-05T02:51:23.881090+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/FPYU3GXUM5KZ7TR55MSOIBXMDA","json":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA.json","graph_json":"https://pith.science/api/pith-number/FPYU3GXUM5KZ7TR55MSOIBXMDA/graph.json","events_json":"https://pith.science/api/pith-number/FPYU3GXUM5KZ7TR55MSOIBXMDA/events.json","paper":"https://pith.science/paper/FPYU3GXU"},"agent_actions":{"view_html":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA","download_json":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA.json","view_paper":"https://pith.science/paper/FPYU3GXU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.08006&json=true","fetch_graph":"https://pith.science/api/pith-number/FPYU3GXUM5KZ7TR55MSOIBXMDA/graph.json","fetch_events":"https://pith.science/api/pith-number/FPYU3GXUM5KZ7TR55MSOIBXMDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA/action/storage_attestation","attest_author":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA/action/author_attestation","sign_citation":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA/action/citation_signature","submit_replication":"https://pith.science/pith/FPYU3GXUM5KZ7TR55MSOIBXMDA/action/replication_record"}},"created_at":"2026-07-05T02:51:23.881090+00:00","updated_at":"2026-07-05T02:51:23.881090+00:00"}