{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ES7FGHQK62G7NT5IJSVK7WXJEL","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":"746b3e2be63b6e7e5b6e45ef74397d7aabf256c5e2d183b24846eb677671b445","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-06-02T12:52:38Z","title_canon_sha256":"14d65a5d3be5464f753272041e8bcd39b0ab4335c60b1c78bee0d9a2382fa872"},"schema_version":"1.0","source":{"id":"2306.01505","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.01505","created_at":"2026-07-05T06:29:02Z"},{"alias_kind":"arxiv_version","alias_value":"2306.01505v2","created_at":"2026-07-05T06:29:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.01505","created_at":"2026-07-05T06:29:02Z"},{"alias_kind":"pith_short_12","alias_value":"ES7FGHQK62G7","created_at":"2026-07-05T06:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"ES7FGHQK62G7NT5I","created_at":"2026-07-05T06:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"ES7FGHQK","created_at":"2026-07-05T06:29:02Z"}],"graph_snapshots":[{"event_id":"sha256:4b75ebccf5355fc7a8a7b439b196747e9866381790b288cfdf21ea77d50ed92b","target":"graph","created_at":"2026-07-05T06:29:02Z","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/2306.01505/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations in a supervised manner. SACL applies contrast-aware adversarial training to generate worst-case samples and uses joint class-spread contrastive learning to extract structured representations. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversari","authors_text":"Dou Hu, Lingwei Wei, Songlin Hu, Wei Zhou, Yinan Bao","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-06-02T12:52:38Z","title":"Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.01505","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":"sha256:250ea23e93d98117df9ec69e387eee8643fd8f72bab29fc670f4e589243d50fe","target":"record","created_at":"2026-07-05T06:29:02Z","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":"746b3e2be63b6e7e5b6e45ef74397d7aabf256c5e2d183b24846eb677671b445","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-06-02T12:52:38Z","title_canon_sha256":"14d65a5d3be5464f753272041e8bcd39b0ab4335c60b1c78bee0d9a2382fa872"},"schema_version":"1.0","source":{"id":"2306.01505","kind":"arxiv","version":2}},"canonical_sha256":"24be531e0af68df6cfa84caaafdae922ef509bb15ab282d0d8abc45a4ab4f320","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24be531e0af68df6cfa84caaafdae922ef509bb15ab282d0d8abc45a4ab4f320","first_computed_at":"2026-07-05T06:29:02.222134Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:29:02.222134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xPv9LUy3oS+SToFjMP37gfrMVrYKx024Mpq8upTSXJ9qWvqvAAtoI5XMRtJUY4OyXGyYK5AA4S6SQWjrnq24Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:29:02.222667Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.01505","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:250ea23e93d98117df9ec69e387eee8643fd8f72bab29fc670f4e589243d50fe","sha256:4b75ebccf5355fc7a8a7b439b196747e9866381790b288cfdf21ea77d50ed92b"],"state_sha256":"f903a24a531bcf888ee13309a11a03b834b51e5fecc76f02d42934336445225b"}