{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UB3KJYAYGZXDQXFMDVM2YL7Y4P","short_pith_number":"pith:UB3KJYAY","schema_version":"1.0","canonical_sha256":"a076a4e018366e385cac1d59ac2ff8e3c8265d377136656ff3589a363c931ef3","source":{"kind":"arxiv","id":"2311.10945","version":1},"attestation_state":"computed","paper":{"title":"An Empirical Bayes Framework for Open-Domain Dialogue Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jing Yang Lee, Kong Aik Lee, Woon-Seng Gan","submitted_at":"2023-11-18T02:48:41Z","abstract_excerpt":"To engage human users in meaningful conversation, open-domain dialogue agents are required to generate diverse and contextually coherent dialogue. Despite recent advancements, which can be attributed to the usage of pretrained language models, the generation of diverse and coherent dialogue remains an open research problem. A popular approach to address this issue involves the adaptation of variational frameworks. However, while these approaches successfully improve diversity, they tend to compromise on contextual coherence. Hence, we propose the Bayesian Open-domain Dialogue with Empirical Ba"},"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":"2311.10945","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-18T02:48:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"63e875d41522418f251dc56eac314647a3c256fcda7b3cb8f761dd7864d51ea1","abstract_canon_sha256":"071b04621e06cf4288048469b66b5404c4edffb116da906d8d455bab7b11b58c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:14:25.122670Z","signature_b64":"kLDHNpJLXQju06F+YjaGoCt5zscsghIuwN3aFeiKFzcMtSLtPkb6TqIWJ5qmXlOFHummE0uy+7b9jmYfpFe7Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a076a4e018366e385cac1d59ac2ff8e3c8265d377136656ff3589a363c931ef3","last_reissued_at":"2026-07-05T07:14:25.122205Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:14:25.122205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Bayes Framework for Open-Domain Dialogue Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jing Yang Lee, Kong Aik Lee, Woon-Seng Gan","submitted_at":"2023-11-18T02:48:41Z","abstract_excerpt":"To engage human users in meaningful conversation, open-domain dialogue agents are required to generate diverse and contextually coherent dialogue. Despite recent advancements, which can be attributed to the usage of pretrained language models, the generation of diverse and coherent dialogue remains an open research problem. A popular approach to address this issue involves the adaptation of variational frameworks. However, while these approaches successfully improve diversity, they tend to compromise on contextual coherence. Hence, we propose the Bayesian Open-domain Dialogue with Empirical Ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10945","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/2311.10945/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":"2311.10945","created_at":"2026-07-05T07:14:25.122269+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.10945v1","created_at":"2026-07-05T07:14:25.122269+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10945","created_at":"2026-07-05T07:14:25.122269+00:00"},{"alias_kind":"pith_short_12","alias_value":"UB3KJYAYGZXD","created_at":"2026-07-05T07:14:25.122269+00:00"},{"alias_kind":"pith_short_16","alias_value":"UB3KJYAYGZXDQXFM","created_at":"2026-07-05T07:14:25.122269+00:00"},{"alias_kind":"pith_short_8","alias_value":"UB3KJYAY","created_at":"2026-07-05T07:14:25.122269+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.03343","citing_title":"Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P","json":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P.json","graph_json":"https://pith.science/api/pith-number/UB3KJYAYGZXDQXFMDVM2YL7Y4P/graph.json","events_json":"https://pith.science/api/pith-number/UB3KJYAYGZXDQXFMDVM2YL7Y4P/events.json","paper":"https://pith.science/paper/UB3KJYAY"},"agent_actions":{"view_html":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P","download_json":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P.json","view_paper":"https://pith.science/paper/UB3KJYAY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.10945&json=true","fetch_graph":"https://pith.science/api/pith-number/UB3KJYAYGZXDQXFMDVM2YL7Y4P/graph.json","fetch_events":"https://pith.science/api/pith-number/UB3KJYAYGZXDQXFMDVM2YL7Y4P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P/action/storage_attestation","attest_author":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P/action/author_attestation","sign_citation":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P/action/citation_signature","submit_replication":"https://pith.science/pith/UB3KJYAYGZXDQXFMDVM2YL7Y4P/action/replication_record"}},"created_at":"2026-07-05T07:14:25.122269+00:00","updated_at":"2026-07-05T07:14:25.122269+00:00"}