{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:44LS6FDFZ5M6F5HK4CSKD2FL3Y","short_pith_number":"pith:44LS6FDF","schema_version":"1.0","canonical_sha256":"e7172f1465cf59e2f4eae0a4a1e8abde0df12c484cdf4ec996860766127699ca","source":{"kind":"arxiv","id":"2106.01978","version":2},"attestation_state":"computed","paper":{"title":"DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dou Hu, Lingwei Wei, Xiaoyong Huai","submitted_at":"2021-06-03T16:47:38Z","abstract_excerpt":"Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to ext"},"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":"2106.01978","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-03T16:47:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a8fa7cd41f56891972defaa86cfb1556af606ea08b4dc8ee6e56de29c4d72c58","abstract_canon_sha256":"bb1ec881cebff204f08433b73c620ae85c7c2e24db3d04485d9b7af3925a3d12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:45.727127Z","signature_b64":"IszvJKXdqVbCGKftkiIlAFyqh5I/2xZKnHYIb48sxMnUaV4RE64CL5d2q0aGpyNxmCbmL10fxUezEYy7dPaQAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7172f1465cf59e2f4eae0a4a1e8abde0df12c484cdf4ec996860766127699ca","last_reissued_at":"2026-07-05T02:47:45.726652Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:45.726652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dou Hu, Lingwei Wei, Xiaoyong Huai","submitted_at":"2021-06-03T16:47:38Z","abstract_excerpt":"Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.01978","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/2106.01978/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":"2106.01978","created_at":"2026-07-05T02:47:45.726712+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.01978v2","created_at":"2026-07-05T02:47:45.726712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.01978","created_at":"2026-07-05T02:47:45.726712+00:00"},{"alias_kind":"pith_short_12","alias_value":"44LS6FDFZ5M6","created_at":"2026-07-05T02:47:45.726712+00:00"},{"alias_kind":"pith_short_16","alias_value":"44LS6FDFZ5M6F5HK","created_at":"2026-07-05T02:47:45.726712+00:00"},{"alias_kind":"pith_short_8","alias_value":"44LS6FDF","created_at":"2026-07-05T02:47:45.726712+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y","json":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y.json","graph_json":"https://pith.science/api/pith-number/44LS6FDFZ5M6F5HK4CSKD2FL3Y/graph.json","events_json":"https://pith.science/api/pith-number/44LS6FDFZ5M6F5HK4CSKD2FL3Y/events.json","paper":"https://pith.science/paper/44LS6FDF"},"agent_actions":{"view_html":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y","download_json":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y.json","view_paper":"https://pith.science/paper/44LS6FDF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.01978&json=true","fetch_graph":"https://pith.science/api/pith-number/44LS6FDFZ5M6F5HK4CSKD2FL3Y/graph.json","fetch_events":"https://pith.science/api/pith-number/44LS6FDFZ5M6F5HK4CSKD2FL3Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y/action/storage_attestation","attest_author":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y/action/author_attestation","sign_citation":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y/action/citation_signature","submit_replication":"https://pith.science/pith/44LS6FDFZ5M6F5HK4CSKD2FL3Y/action/replication_record"}},"created_at":"2026-07-05T02:47:45.726712+00:00","updated_at":"2026-07-05T02:47:45.726712+00:00"}