{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RKYFWDMYHZKD4PVNPTMOT5VZYW","short_pith_number":"pith:RKYFWDMY","schema_version":"1.0","canonical_sha256":"8ab05b0d983e543e3ead7cd8e9f6b9c5918555b8a210911714e1a054aa334847","source":{"kind":"arxiv","id":"2403.07260","version":2},"attestation_state":"computed","paper":{"title":"LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bingquan Li, Junjie Wu, Lili Shan, Meishan Zhang, Yulin Wu, Yumeng Fu, Zhongjie Wang","submitted_at":"2024-03-12T02:37:11Z","abstract_excerpt":"Emotion recognition in conversation (ERC), the task of discerning human emotions for each utterance within a conversation, has garnered significant attention in human-computer interaction systems. Previous ERC studies focus on speaker-specific information that predominantly stems from relationships among utterances, which lacks sufficient information around conversations. Recent research in ERC has sought to exploit pre-trained large language models (LLMs) with speaker modelling to comprehend emotional states. Although these methods have achieved encouraging results, the extracted speaker-spec"},"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":"2403.07260","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-12T02:37:11Z","cross_cats_sorted":[],"title_canon_sha256":"df2bea8c5ca30e26038730515d2f526de01afc77d9a32d649657f1249284a1a6","abstract_canon_sha256":"a4f5a6bbedfd1f5e6e9917f7b46be1189f5cd831304ace0951e0d74efb275469"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:44.737065Z","signature_b64":"5HQkV5chUikXjR+7DlUFVf9uPd+4xBjh2RGyqCZZWJo9PjW0Ih/95ZF76EZcn+rnFTcRc/7o/9iPSdVafjo7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ab05b0d983e543e3ead7cd8e9f6b9c5918555b8a210911714e1a054aa334847","last_reissued_at":"2026-07-05T10:22:44.736351Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:44.736351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bingquan Li, Junjie Wu, Lili Shan, Meishan Zhang, Yulin Wu, Yumeng Fu, Zhongjie Wang","submitted_at":"2024-03-12T02:37:11Z","abstract_excerpt":"Emotion recognition in conversation (ERC), the task of discerning human emotions for each utterance within a conversation, has garnered significant attention in human-computer interaction systems. Previous ERC studies focus on speaker-specific information that predominantly stems from relationships among utterances, which lacks sufficient information around conversations. Recent research in ERC has sought to exploit pre-trained large language models (LLMs) with speaker modelling to comprehend emotional states. Although these methods have achieved encouraging results, the extracted speaker-spec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07260","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/2403.07260/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":"2403.07260","created_at":"2026-07-05T10:22:44.736440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.07260v2","created_at":"2026-07-05T10:22:44.736440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07260","created_at":"2026-07-05T10:22:44.736440+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKYFWDMYHZKD","created_at":"2026-07-05T10:22:44.736440+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKYFWDMYHZKD4PVN","created_at":"2026-07-05T10:22:44.736440+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKYFWDMY","created_at":"2026-07-05T10:22:44.736440+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00860","citing_title":"GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW","json":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW.json","graph_json":"https://pith.science/api/pith-number/RKYFWDMYHZKD4PVNPTMOT5VZYW/graph.json","events_json":"https://pith.science/api/pith-number/RKYFWDMYHZKD4PVNPTMOT5VZYW/events.json","paper":"https://pith.science/paper/RKYFWDMY"},"agent_actions":{"view_html":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW","download_json":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW.json","view_paper":"https://pith.science/paper/RKYFWDMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.07260&json=true","fetch_graph":"https://pith.science/api/pith-number/RKYFWDMYHZKD4PVNPTMOT5VZYW/graph.json","fetch_events":"https://pith.science/api/pith-number/RKYFWDMYHZKD4PVNPTMOT5VZYW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW/action/storage_attestation","attest_author":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW/action/author_attestation","sign_citation":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW/action/citation_signature","submit_replication":"https://pith.science/pith/RKYFWDMYHZKD4PVNPTMOT5VZYW/action/replication_record"}},"created_at":"2026-07-05T10:22:44.736440+00:00","updated_at":"2026-07-05T10:22:44.736440+00:00"}