{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:GVPV4LBHVMMF6GNPH36KYD55CO","short_pith_number":"pith:GVPV4LBH","canonical_record":{"source":{"id":"2104.10117","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-20T16:55:15Z","cross_cats_sorted":[],"title_canon_sha256":"c308953c920bb7ba8485267bc86ead228cf54fb69a618b742fec0d44b1c52c9f","abstract_canon_sha256":"88a6c8f4b236f6e600718458db803fc00aec1e380e9f40eeade8f51e69002d6c"},"schema_version":"1.0"},"canonical_sha256":"355f5e2c27ab185f19af3efcac0fbd13b85e9d0e8b08eeb901e8e4eec403bdb6","source":{"kind":"arxiv","id":"2104.10117","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.10117","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"arxiv_version","alias_value":"2104.10117v1","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.10117","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"pith_short_12","alias_value":"GVPV4LBHVMMF","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"pith_short_16","alias_value":"GVPV4LBHVMMF6GNP","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"pith_short_8","alias_value":"GVPV4LBH","created_at":"2026-07-05T02:33:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:GVPV4LBHVMMF6GNPH36KYD55CO","target":"record","payload":{"canonical_record":{"source":{"id":"2104.10117","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-20T16:55:15Z","cross_cats_sorted":[],"title_canon_sha256":"c308953c920bb7ba8485267bc86ead228cf54fb69a618b742fec0d44b1c52c9f","abstract_canon_sha256":"88a6c8f4b236f6e600718458db803fc00aec1e380e9f40eeade8f51e69002d6c"},"schema_version":"1.0"},"canonical_sha256":"355f5e2c27ab185f19af3efcac0fbd13b85e9d0e8b08eeb901e8e4eec403bdb6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:33:39.962365Z","signature_b64":"JFjoNdjWFv508Od1sVmslZBZqJW1Y9NgtjB+D4a3jKAthb2DQuL2cF5UGV8FhNh7cFko8r0WyKFYA425KfzeCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"355f5e2c27ab185f19af3efcac0fbd13b85e9d0e8b08eeb901e8e4eec403bdb6","last_reissued_at":"2026-07-05T02:33:39.961949Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:33:39.961949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.10117","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:33:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wVf4DHlT1ZR0BUtQKq/18Fqkw/zFlzjOz4YV2lBpE9Qwf+xzirBT3C78xfotFmDWmuuk/1UfTJyVISfvMcsvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T06:57:36.525767Z"},"content_sha256":"c4f55cd690d89d8a400b5bd33901c3eab3ac2af6277bc2e17e314443a4331e4c","schema_version":"1.0","event_id":"sha256:c4f55cd690d89d8a400b5bd33901c3eab3ac2af6277bc2e17e314443a4331e4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:GVPV4LBHVMMF6GNPH36KYD55CO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Cognitive Models of Emotions with Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jinho Choi, Yuting Guo","submitted_at":"2021-04-20T16:55:15Z","abstract_excerpt":"We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions that can be used to computationally describe psychological models of emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model that enables to interpret dynamically learned representations optimized for an emotion classification task. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions. Our layer analysis can derive an emotion graph to depict hierarchical relations amon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.10117","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/2104.10117/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":"2026-07-05T02:33:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iNs44mPRRhuIX5LLZsbMfSIP66+k3aMnswzO7dOfQKUgFQGfspY1F4xik2cFteVrLAEE0V8GzhNHCvEE/fnyDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T06:57:36.526273Z"},"content_sha256":"446898fc30692e1acce08ab3ec5f266aa8de424c70c09211b22f0769b2ea0a13","schema_version":"1.0","event_id":"sha256:446898fc30692e1acce08ab3ec5f266aa8de424c70c09211b22f0769b2ea0a13"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GVPV4LBHVMMF6GNPH36KYD55CO/bundle.json","state_url":"https://pith.science/pith/GVPV4LBHVMMF6GNPH36KYD55CO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GVPV4LBHVMMF6GNPH36KYD55CO/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":"2026-08-20T06:57:36Z","links":{"resolver":"https://pith.science/pith/GVPV4LBHVMMF6GNPH36KYD55CO","bundle":"https://pith.science/pith/GVPV4LBHVMMF6GNPH36KYD55CO/bundle.json","state":"https://pith.science/pith/GVPV4LBHVMMF6GNPH36KYD55CO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GVPV4LBHVMMF6GNPH36KYD55CO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:GVPV4LBHVMMF6GNPH36KYD55CO","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":"88a6c8f4b236f6e600718458db803fc00aec1e380e9f40eeade8f51e69002d6c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-20T16:55:15Z","title_canon_sha256":"c308953c920bb7ba8485267bc86ead228cf54fb69a618b742fec0d44b1c52c9f"},"schema_version":"1.0","source":{"id":"2104.10117","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.10117","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"arxiv_version","alias_value":"2104.10117v1","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.10117","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"pith_short_12","alias_value":"GVPV4LBHVMMF","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"pith_short_16","alias_value":"GVPV4LBHVMMF6GNP","created_at":"2026-07-05T02:33:39Z"},{"alias_kind":"pith_short_8","alias_value":"GVPV4LBH","created_at":"2026-07-05T02:33:39Z"}],"graph_snapshots":[{"event_id":"sha256:446898fc30692e1acce08ab3ec5f266aa8de424c70c09211b22f0769b2ea0a13","target":"graph","created_at":"2026-07-05T02:33:39Z","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/2104.10117/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions that can be used to computationally describe psychological models of emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model that enables to interpret dynamically learned representations optimized for an emotion classification task. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions. Our layer analysis can derive an emotion graph to depict hierarchical relations amon","authors_text":"Jinho Choi, Yuting Guo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-20T16:55:15Z","title":"Enhancing Cognitive Models of Emotions with Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.10117","kind":"arxiv","version":1},"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:c4f55cd690d89d8a400b5bd33901c3eab3ac2af6277bc2e17e314443a4331e4c","target":"record","created_at":"2026-07-05T02:33:39Z","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":"88a6c8f4b236f6e600718458db803fc00aec1e380e9f40eeade8f51e69002d6c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-20T16:55:15Z","title_canon_sha256":"c308953c920bb7ba8485267bc86ead228cf54fb69a618b742fec0d44b1c52c9f"},"schema_version":"1.0","source":{"id":"2104.10117","kind":"arxiv","version":1}},"canonical_sha256":"355f5e2c27ab185f19af3efcac0fbd13b85e9d0e8b08eeb901e8e4eec403bdb6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"355f5e2c27ab185f19af3efcac0fbd13b85e9d0e8b08eeb901e8e4eec403bdb6","first_computed_at":"2026-07-05T02:33:39.961949Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:33:39.961949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JFjoNdjWFv508Od1sVmslZBZqJW1Y9NgtjB+D4a3jKAthb2DQuL2cF5UGV8FhNh7cFko8r0WyKFYA425KfzeCw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:33:39.962365Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.10117","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4f55cd690d89d8a400b5bd33901c3eab3ac2af6277bc2e17e314443a4331e4c","sha256:446898fc30692e1acce08ab3ec5f266aa8de424c70c09211b22f0769b2ea0a13"],"state_sha256":"96acd85eea26f11e74e9f232eafa2cc43eb676a4d55c615eea7c8c77d9a7ddde"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gYO3OUhLgv85G5r7sYaDbDv60J9AfMbE9WkIHB6YYJgfsFD1NK1u5vior3amtu+0VpX+i4wLV0BDNyYNf/WuDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T06:57:36.531410Z","bundle_sha256":"9713e664e1dd1c530b4f47651bebb78aac75b48a3a846c28468135ea64074c44"}}