{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:C5APVGU3HP4QB3LUTUSPPYEYTB","short_pith_number":"pith:C5APVGU3","canonical_record":{"source":{"id":"2209.14627","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-09-29T08:41:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9ce094ace4d43f6072f3c67e3d6ce218c43c3ee477d1bea7444472810fd3f150","abstract_canon_sha256":"41939dc22443b0cc7bc1d32a87f0f68fa6713fff12dc72e7f9588d930c3ba941"},"schema_version":"1.0"},"canonical_sha256":"1740fa9a9b3bf900ed749d24f7e09898522138cc28d93192cf4fcd17afbdd81e","source":{"kind":"arxiv","id":"2209.14627","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.14627","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"arxiv_version","alias_value":"2209.14627v2","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.14627","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"pith_short_12","alias_value":"C5APVGU3HP4Q","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"pith_short_16","alias_value":"C5APVGU3HP4QB3LU","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"pith_short_8","alias_value":"C5APVGU3","created_at":"2026-07-05T05:54:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:C5APVGU3HP4QB3LUTUSPPYEYTB","target":"record","payload":{"canonical_record":{"source":{"id":"2209.14627","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-09-29T08:41:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9ce094ace4d43f6072f3c67e3d6ce218c43c3ee477d1bea7444472810fd3f150","abstract_canon_sha256":"41939dc22443b0cc7bc1d32a87f0f68fa6713fff12dc72e7f9588d930c3ba941"},"schema_version":"1.0"},"canonical_sha256":"1740fa9a9b3bf900ed749d24f7e09898522138cc28d93192cf4fcd17afbdd81e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:31.544945Z","signature_b64":"+NXIGVY/QpCTwcyMYwgUjl7X7v6RqNXSunnAtbquFKI+iFESdiBemtDFqOL3j5iiJ0Le/FyggxObENBPCzF1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1740fa9a9b3bf900ed749d24f7e09898522138cc28d93192cf4fcd17afbdd81e","last_reissued_at":"2026-07-05T05:54:31.544397Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:31.544397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.14627","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:54:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TjOEv7xWcIqlHKuX30MKY6hg2Xr3ZY8oFKwhUK13oe8JNdUgMeoQ9yes8+ySCWYY2q2vHNyHt+DQedR8OcGLCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T18:15:34.128101Z"},"content_sha256":"fff909f0da61cec83647b6fc9f99a35b73d4d06b904e9206c119ab55d4458984","schema_version":"1.0","event_id":"sha256:fff909f0da61cec83647b6fc9f99a35b73d4d06b904e9206c119ab55d4458984"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:C5APVGU3HP4QB3LUTUSPPYEYTB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Lili Mou, Yanshuai Cao, Yongchang Hao, Yuqiao Wen","submitted_at":"2022-09-29T08:41:32Z","abstract_excerpt":"Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion. Despite the recent success of super large dialogue systems such as ChatGPT, using medium-to-small-sized dialogue systems remains the common practice as they are more lightweight and accessible; however, generating diverse dialogue responses is challenging, especially with smaller models. In this work, we propose an Equal-size Hard Expectation--Maximization (EqHard-EM) algorithm to train a multi-decoder model for diverse dialogue generation. Our algorithm assigns a sample to a decod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.14627","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/2209.14627/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:54:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0A0RboavsEdcna9/Oexglw0K9hFahVk2uguvyex1Rq7U4cnImDdm3yP/aOSRiDq3ETV0vh4an5ZBVaCbhsr2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T18:15:34.128711Z"},"content_sha256":"60c243d1f881b8936b3fc96abc6087f9bf56fc9230079e7bb9695c1d6677adc5","schema_version":"1.0","event_id":"sha256:60c243d1f881b8936b3fc96abc6087f9bf56fc9230079e7bb9695c1d6677adc5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C5APVGU3HP4QB3LUTUSPPYEYTB/bundle.json","state_url":"https://pith.science/pith/C5APVGU3HP4QB3LUTUSPPYEYTB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C5APVGU3HP4QB3LUTUSPPYEYTB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T18:15:34Z","links":{"resolver":"https://pith.science/pith/C5APVGU3HP4QB3LUTUSPPYEYTB","bundle":"https://pith.science/pith/C5APVGU3HP4QB3LUTUSPPYEYTB/bundle.json","state":"https://pith.science/pith/C5APVGU3HP4QB3LUTUSPPYEYTB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C5APVGU3HP4QB3LUTUSPPYEYTB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:C5APVGU3HP4QB3LUTUSPPYEYTB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"41939dc22443b0cc7bc1d32a87f0f68fa6713fff12dc72e7f9588d930c3ba941","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-09-29T08:41:32Z","title_canon_sha256":"9ce094ace4d43f6072f3c67e3d6ce218c43c3ee477d1bea7444472810fd3f150"},"schema_version":"1.0","source":{"id":"2209.14627","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.14627","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"arxiv_version","alias_value":"2209.14627v2","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.14627","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"pith_short_12","alias_value":"C5APVGU3HP4Q","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"pith_short_16","alias_value":"C5APVGU3HP4QB3LU","created_at":"2026-07-05T05:54:31Z"},{"alias_kind":"pith_short_8","alias_value":"C5APVGU3","created_at":"2026-07-05T05:54:31Z"}],"graph_snapshots":[{"event_id":"sha256:60c243d1f881b8936b3fc96abc6087f9bf56fc9230079e7bb9695c1d6677adc5","target":"graph","created_at":"2026-07-05T05:54:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2209.14627/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion. Despite the recent success of super large dialogue systems such as ChatGPT, using medium-to-small-sized dialogue systems remains the common practice as they are more lightweight and accessible; however, generating diverse dialogue responses is challenging, especially with smaller models. In this work, we propose an Equal-size Hard Expectation--Maximization (EqHard-EM) algorithm to train a multi-decoder model for diverse dialogue generation. Our algorithm assigns a sample to a decod","authors_text":"Lili Mou, Yanshuai Cao, Yongchang Hao, Yuqiao Wen","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-09-29T08:41:32Z","title":"An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.14627","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fff909f0da61cec83647b6fc9f99a35b73d4d06b904e9206c119ab55d4458984","target":"record","created_at":"2026-07-05T05:54:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"41939dc22443b0cc7bc1d32a87f0f68fa6713fff12dc72e7f9588d930c3ba941","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-09-29T08:41:32Z","title_canon_sha256":"9ce094ace4d43f6072f3c67e3d6ce218c43c3ee477d1bea7444472810fd3f150"},"schema_version":"1.0","source":{"id":"2209.14627","kind":"arxiv","version":2}},"canonical_sha256":"1740fa9a9b3bf900ed749d24f7e09898522138cc28d93192cf4fcd17afbdd81e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1740fa9a9b3bf900ed749d24f7e09898522138cc28d93192cf4fcd17afbdd81e","first_computed_at":"2026-07-05T05:54:31.544397Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:54:31.544397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+NXIGVY/QpCTwcyMYwgUjl7X7v6RqNXSunnAtbquFKI+iFESdiBemtDFqOL3j5iiJ0Le/FyggxObENBPCzF1Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:54:31.544945Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.14627","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fff909f0da61cec83647b6fc9f99a35b73d4d06b904e9206c119ab55d4458984","sha256:60c243d1f881b8936b3fc96abc6087f9bf56fc9230079e7bb9695c1d6677adc5"],"state_sha256":"225d05d57abb84c94cbbae3a79f4e5815b764fb2eecb20a7d3b194bbe02a903a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Il1Xod8/rSrvWSTUnOTVOy3oiSj4Vgr8gtewjwJHGBzRUlsFdAtJ5L6/yXGCSqGQl/mwe9j//d5Ul1ioJ+C0CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T18:15:34.134233Z","bundle_sha256":"783078c51ef2bf02589ac513a2bd0d3377a588b6ca6b7619238b290b333114f1"}}