{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4L53C74HJKMA6AREKNRDHYC4WM","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":"44f04d8d00ff33ead4c18afb6f5ddbbe1c733046cba0ceb41300c2806daa618a","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-31T14:33:30Z","title_canon_sha256":"14b605bed725ae8174fd287c95f581f4c6e78d7e8d9b70a14360f503543ed441"},"schema_version":"1.0","source":{"id":"2310.20494","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.20494","created_at":"2026-07-05T07:07:31Z"},{"alias_kind":"arxiv_version","alias_value":"2310.20494v1","created_at":"2026-07-05T07:07:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20494","created_at":"2026-07-05T07:07:31Z"},{"alias_kind":"pith_short_12","alias_value":"4L53C74HJKMA","created_at":"2026-07-05T07:07:31Z"},{"alias_kind":"pith_short_16","alias_value":"4L53C74HJKMA6ARE","created_at":"2026-07-05T07:07:31Z"},{"alias_kind":"pith_short_8","alias_value":"4L53C74H","created_at":"2026-07-05T07:07:31Z"}],"graph_snapshots":[{"event_id":"sha256:bf50e8f0a199a1fa3f55556f7d2e7b6ed7c7ce988fc0457d953144b917edc91b","target":"graph","created_at":"2026-07-05T07:07:31Z","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/2310.20494/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines. Existing studies focus mainly on capturing context- and speaker-sensitive dependencies on the textual modality but ignore the significance of multimodal information. Different from emotion recognition in textual conversations, capturing intra- and inter-modal interactions between utterances, learning weights between different modalities, and enhancing modal representations play important roles in multimodal ERC. In this paper, we prop","authors_text":"Bo Xu, Bo Zhang, Hongfei Lin, Hui Ma, Jian Wang, Yijia Zhang","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-31T14:33:30Z","title":"A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20494","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:bf990ca352f156c92d2db490bae8c9af6d8e015b8fcb2c75aa4c6a561d0342d1","target":"record","created_at":"2026-07-05T07:07:31Z","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":"44f04d8d00ff33ead4c18afb6f5ddbbe1c733046cba0ceb41300c2806daa618a","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-31T14:33:30Z","title_canon_sha256":"14b605bed725ae8174fd287c95f581f4c6e78d7e8d9b70a14360f503543ed441"},"schema_version":"1.0","source":{"id":"2310.20494","kind":"arxiv","version":1}},"canonical_sha256":"e2fbb17f874a980f0224536233e05cb32dc03f9443cdf08a61bcf2258f0f8cd6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2fbb17f874a980f0224536233e05cb32dc03f9443cdf08a61bcf2258f0f8cd6","first_computed_at":"2026-07-05T07:07:31.082091Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:07:31.082091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8Uwf1EQUpMmgHsCaq1Pf3jQcx4TnDBAjlpOno1Op/300Rg3aWQ98Xb/ygLB/5IqzXT89EIDqO6eMBonXheGRCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:07:31.082589Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.20494","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf990ca352f156c92d2db490bae8c9af6d8e015b8fcb2c75aa4c6a561d0342d1","sha256:bf50e8f0a199a1fa3f55556f7d2e7b6ed7c7ce988fc0457d953144b917edc91b"],"state_sha256":"766ab95975e794155b885a0b67343b1bfadb45bc6869c2265b70d9213d887a9b"}