{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2Y7PY4NLEKIBYKGWJZHPA7RVT6","short_pith_number":"pith:2Y7PY4NL","schema_version":"1.0","canonical_sha256":"d63efc71ab22901c28d64e4ef07e359f90a97392a3ab30ac21e95175d9d13d66","source":{"kind":"arxiv","id":"2108.12009","version":1},"attestation_state":"computed","paper":{"title":"EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Piek Vossen, Taewoon Kim","submitted_at":"2021-08-26T19:34:26Z","abstract_excerpt":"We present EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa, a simple yet expressive scheme of solving the ERC (emotion recognition in conversation) task. By simply prepending speaker names to utterances and inserting separation tokens between the utterances in a dialogue, EmoBERTa can learn intra- and inter- speaker states and context to predict the emotion of a current speaker, in an end-to-end manner. Our experiments show that we reach a new state of the art on the two popular ERC datasets using a basic and straight-forward approach. We've open sourced our code and m"},"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":"2108.12009","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-08-26T19:34:26Z","cross_cats_sorted":[],"title_canon_sha256":"e0101c6b9a7ba8b994dc6c5e704971d780fd3d6e89f4277c9c20f521e43d329c","abstract_canon_sha256":"025bf6193c00c4f6844a5701acfceb17ab9de68bb6330a85464c5afa54419402"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:09:20.780414Z","signature_b64":"OcNOby58moPFf+8TwRiNakzoAwYwvEEZVKwsz7+nUhrVr7Ai6jtgLCMzleiZOGPmkmKSUBN7KhZEot9KGH8xBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d63efc71ab22901c28d64e4ef07e359f90a97392a3ab30ac21e95175d9d13d66","last_reissued_at":"2026-07-05T03:09:20.779942Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:09:20.779942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Piek Vossen, Taewoon Kim","submitted_at":"2021-08-26T19:34:26Z","abstract_excerpt":"We present EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa, a simple yet expressive scheme of solving the ERC (emotion recognition in conversation) task. By simply prepending speaker names to utterances and inserting separation tokens between the utterances in a dialogue, EmoBERTa can learn intra- and inter- speaker states and context to predict the emotion of a current speaker, in an end-to-end manner. Our experiments show that we reach a new state of the art on the two popular ERC datasets using a basic and straight-forward approach. We've open sourced our code and m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.12009","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/2108.12009/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":"2108.12009","created_at":"2026-07-05T03:09:20.780000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.12009v1","created_at":"2026-07-05T03:09:20.780000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.12009","created_at":"2026-07-05T03:09:20.780000+00:00"},{"alias_kind":"pith_short_12","alias_value":"2Y7PY4NLEKIB","created_at":"2026-07-05T03:09:20.780000+00:00"},{"alias_kind":"pith_short_16","alias_value":"2Y7PY4NLEKIBYKGW","created_at":"2026-07-05T03:09:20.780000+00:00"},{"alias_kind":"pith_short_8","alias_value":"2Y7PY4NL","created_at":"2026-07-05T03:09:20.780000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18547","citing_title":"VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversation","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6","json":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6.json","graph_json":"https://pith.science/api/pith-number/2Y7PY4NLEKIBYKGWJZHPA7RVT6/graph.json","events_json":"https://pith.science/api/pith-number/2Y7PY4NLEKIBYKGWJZHPA7RVT6/events.json","paper":"https://pith.science/paper/2Y7PY4NL"},"agent_actions":{"view_html":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6","download_json":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6.json","view_paper":"https://pith.science/paper/2Y7PY4NL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.12009&json=true","fetch_graph":"https://pith.science/api/pith-number/2Y7PY4NLEKIBYKGWJZHPA7RVT6/graph.json","fetch_events":"https://pith.science/api/pith-number/2Y7PY4NLEKIBYKGWJZHPA7RVT6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6/action/storage_attestation","attest_author":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6/action/author_attestation","sign_citation":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6/action/citation_signature","submit_replication":"https://pith.science/pith/2Y7PY4NLEKIBYKGWJZHPA7RVT6/action/replication_record"}},"created_at":"2026-07-05T03:09:20.780000+00:00","updated_at":"2026-07-05T03:09:20.780000+00:00"}