{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZXJ2VKQDE3LQBDNTZP63KKAIXM","short_pith_number":"pith:ZXJ2VKQD","canonical_record":{"source":{"id":"2210.08713","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2022-10-17T03:08:23Z","cross_cats_sorted":[],"title_canon_sha256":"aa8e0c44fbe39208867d1881bead6ccdfa4bd90551f6e0f6550a093770227487","abstract_canon_sha256":"c73c7d71d96e878586f70044b74175e6e33f84c1aebcc597fe780aae79937817"},"schema_version":"1.0"},"canonical_sha256":"cdd3aaaa0326d7008db3cbfdb52808bb19caf9069c4184e42b196b9169e57ea6","source":{"kind":"arxiv","id":"2210.08713","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08713","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08713v2","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08713","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"pith_short_12","alias_value":"ZXJ2VKQDE3LQ","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"pith_short_16","alias_value":"ZXJ2VKQDE3LQBDNT","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"pith_short_8","alias_value":"ZXJ2VKQD","created_at":"2026-07-05T05:08:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZXJ2VKQDE3LQBDNTZP63KKAIXM","target":"record","payload":{"canonical_record":{"source":{"id":"2210.08713","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2022-10-17T03:08:23Z","cross_cats_sorted":[],"title_canon_sha256":"aa8e0c44fbe39208867d1881bead6ccdfa4bd90551f6e0f6550a093770227487","abstract_canon_sha256":"c73c7d71d96e878586f70044b74175e6e33f84c1aebcc597fe780aae79937817"},"schema_version":"1.0"},"canonical_sha256":"cdd3aaaa0326d7008db3cbfdb52808bb19caf9069c4184e42b196b9169e57ea6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:08:18.621737Z","signature_b64":"Xz2pfUlswHTZmc7pKPpscy/o30aKiwulHSbnEpNRFaRDFAeCy9JkNhJwnlxExneRg071fmGA/w2CgYRt1C3GDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdd3aaaa0326d7008db3cbfdb52808bb19caf9069c4184e42b196b9169e57ea6","last_reissued_at":"2026-07-05T05:08:18.621252Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:08:18.621252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.08713","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":"2026-07-05T05:08:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kZr5OS3/J/Ouoh2n9Ua6+TwjYKU8cJoCrBqEjcY35uzrdkMMKegPu5SmEz4Kdcj4hTa8X/9tUjC7J8m9j0w1Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:03:44.934045Z"},"content_sha256":"781d24a0e8aa9efe6fad882ec347f6c4c64bbd78a0801bddbcfecb8996c05365","schema_version":"1.0","event_id":"sha256:781d24a0e8aa9efe6fad882ec347f6c4c64bbd78a0801bddbcfecb8996c05365"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZXJ2VKQDE3LQBDNTZP63KKAIXM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hui Xue, Longtao Huang, Songlin Hu, Xiaohui Song","submitted_at":"2022-10-17T03:08:23Z","abstract_excerpt":"Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC). Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers. In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task. Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08713","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/2210.08713/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":"2026-07-05T05:08:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zH5bZx840cRqh9M3lfemevTxkIZejP2i2VoZzPi2LLnfE+K7UiEk/+P9NeFnYHZ8Aix+WW9WG/rzaEk/PJC/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:03:44.936923Z"},"content_sha256":"3998bdc759f00f75f6c65c02906c55f3f35e67139f7dc8754de72a12206fceaf","schema_version":"1.0","event_id":"sha256:3998bdc759f00f75f6c65c02906c55f3f35e67139f7dc8754de72a12206fceaf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM/bundle.json","state_url":"https://pith.science/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM/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":"2026-08-13T20:03:44Z","links":{"resolver":"https://pith.science/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM","bundle":"https://pith.science/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM/bundle.json","state":"https://pith.science/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZXJ2VKQDE3LQBDNTZP63KKAIXM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZXJ2VKQDE3LQBDNTZP63KKAIXM","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":"c73c7d71d96e878586f70044b74175e6e33f84c1aebcc597fe780aae79937817","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2022-10-17T03:08:23Z","title_canon_sha256":"aa8e0c44fbe39208867d1881bead6ccdfa4bd90551f6e0f6550a093770227487"},"schema_version":"1.0","source":{"id":"2210.08713","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08713","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08713v2","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08713","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"pith_short_12","alias_value":"ZXJ2VKQDE3LQ","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"pith_short_16","alias_value":"ZXJ2VKQDE3LQBDNT","created_at":"2026-07-05T05:08:18Z"},{"alias_kind":"pith_short_8","alias_value":"ZXJ2VKQD","created_at":"2026-07-05T05:08:18Z"}],"graph_snapshots":[{"event_id":"sha256:3998bdc759f00f75f6c65c02906c55f3f35e67139f7dc8754de72a12206fceaf","target":"graph","created_at":"2026-07-05T05:08:18Z","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/2210.08713/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC). Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers. In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task. Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch s","authors_text":"Hui Xue, Longtao Huang, Songlin Hu, Xiaohui Song","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2022-10-17T03:08:23Z","title":"Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08713","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":"sha256:781d24a0e8aa9efe6fad882ec347f6c4c64bbd78a0801bddbcfecb8996c05365","target":"record","created_at":"2026-07-05T05:08:18Z","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":"c73c7d71d96e878586f70044b74175e6e33f84c1aebcc597fe780aae79937817","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2022-10-17T03:08:23Z","title_canon_sha256":"aa8e0c44fbe39208867d1881bead6ccdfa4bd90551f6e0f6550a093770227487"},"schema_version":"1.0","source":{"id":"2210.08713","kind":"arxiv","version":2}},"canonical_sha256":"cdd3aaaa0326d7008db3cbfdb52808bb19caf9069c4184e42b196b9169e57ea6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cdd3aaaa0326d7008db3cbfdb52808bb19caf9069c4184e42b196b9169e57ea6","first_computed_at":"2026-07-05T05:08:18.621252Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:08:18.621252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xz2pfUlswHTZmc7pKPpscy/o30aKiwulHSbnEpNRFaRDFAeCy9JkNhJwnlxExneRg071fmGA/w2CgYRt1C3GDA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:08:18.621737Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.08713","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:781d24a0e8aa9efe6fad882ec347f6c4c64bbd78a0801bddbcfecb8996c05365","sha256:3998bdc759f00f75f6c65c02906c55f3f35e67139f7dc8754de72a12206fceaf"],"state_sha256":"edf56d180fa41b2222cc1fc27f5c22289df0d7170763158a357c91766a10f892"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GJBKyzoA5yDvLe8s+1xBxmxsJrNFj5eaxfipfWUJIheI72wUHzqXDgdP9mqx93LX6brBeQt8PS41S0MslxCdAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T20:03:44.950587Z","bundle_sha256":"187820bfaeac79e849f0514c4f3a5623b6b61e98c26950c699526c647ae193c4"}}