{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SR7XCI6JV636FCKUATASIIWUFD","short_pith_number":"pith:SR7XCI6J","schema_version":"1.0","canonical_sha256":"947f7123c9afb7e2895404c12422d428e7834e6fbf0847869b6742419d724fb0","source":{"kind":"arxiv","id":"1910.07931","version":3},"attestation_state":"computed","paper":{"title":"PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fan Wang, HaiFeng Wang, Huang He, Hua Wu, Siqi Bao","submitted_at":"2019-10-17T14:09:42Z","abstract_excerpt":"Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. "},"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":"1910.07931","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-17T14:09:42Z","cross_cats_sorted":[],"title_canon_sha256":"4838c35b520190987f3f66d5cd38631264d59565cf56ac25caad3c0686a70cfa","abstract_canon_sha256":"440934db95c9626ac751c328db1e65926f46d4be7a7f46dac65be13127634b2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:59:22.912729Z","signature_b64":"LepWW3Rj23FF7dD9d/kIRTnNnHsuFJnoalbY9kWbxTLIX6Mnm8gw0synI4goukjeLrL99w4EjgnQUWpUaXbrCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"947f7123c9afb7e2895404c12422d428e7834e6fbf0847869b6742419d724fb0","last_reissued_at":"2026-07-05T00:59:22.912233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:59:22.912233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fan Wang, HaiFeng Wang, Huang He, Hua Wu, Siqi Bao","submitted_at":"2019-10-17T14:09:42Z","abstract_excerpt":"Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.07931","kind":"arxiv","version":3},"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/1910.07931/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":"1910.07931","created_at":"2026-07-05T00:59:22.912295+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.07931v3","created_at":"2026-07-05T00:59:22.912295+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.07931","created_at":"2026-07-05T00:59:22.912295+00:00"},{"alias_kind":"pith_short_12","alias_value":"SR7XCI6JV636","created_at":"2026-07-05T00:59:22.912295+00:00"},{"alias_kind":"pith_short_16","alias_value":"SR7XCI6JV636FCKU","created_at":"2026-07-05T00:59:22.912295+00:00"},{"alias_kind":"pith_short_8","alias_value":"SR7XCI6J","created_at":"2026-07-05T00:59:22.912295+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.05940","citing_title":"Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD","json":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD.json","graph_json":"https://pith.science/api/pith-number/SR7XCI6JV636FCKUATASIIWUFD/graph.json","events_json":"https://pith.science/api/pith-number/SR7XCI6JV636FCKUATASIIWUFD/events.json","paper":"https://pith.science/paper/SR7XCI6J"},"agent_actions":{"view_html":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD","download_json":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD.json","view_paper":"https://pith.science/paper/SR7XCI6J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.07931&json=true","fetch_graph":"https://pith.science/api/pith-number/SR7XCI6JV636FCKUATASIIWUFD/graph.json","fetch_events":"https://pith.science/api/pith-number/SR7XCI6JV636FCKUATASIIWUFD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD/action/storage_attestation","attest_author":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD/action/author_attestation","sign_citation":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD/action/citation_signature","submit_replication":"https://pith.science/pith/SR7XCI6JV636FCKUATASIIWUFD/action/replication_record"}},"created_at":"2026-07-05T00:59:22.912295+00:00","updated_at":"2026-07-05T00:59:22.912295+00:00"}