{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JE5V3ZR4VEE52Y3JZJWRXTZL53","short_pith_number":"pith:JE5V3ZR4","schema_version":"1.0","canonical_sha256":"493b5de63ca909dd6369ca6d1bcf2beec2d1a147c2ababbf6b376b2e95b210e0","source":{"kind":"arxiv","id":"2504.05824","version":1},"attestation_state":"computed","paper":{"title":"End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiyuan Li, Le Dai, Mingbang Wang, Ruilin Nong, Songhang Deng, Xingzu Liu, Zhang Dong","submitted_at":"2025-04-08T09:06:52Z","abstract_excerpt":"Large-scale coreference resolution presents a significant challenge in natural language processing, necessitating a balance between efficiency and accuracy. In response to this challenge, we introduce an End-to-End Neural Coreference Resolution system tailored for large-scale applications. Our system efficiently identifies and resolves coreference links in text, ensuring minimal computational overhead without compromising on performance. By utilizing advanced neural network architectures, we incorporate various contextual embeddings and attention mechanisms, which enhance the quality of predic"},"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":"2504.05824","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-08T09:06:52Z","cross_cats_sorted":[],"title_canon_sha256":"89ab0cb1ad9b0c75779f4ac1d673d8ab5202857748f737774c1b21138821d12d","abstract_canon_sha256":"ab450758e330ce7f9a7c149187a10f170d9e592af3f7a1252cdbfebd046d44c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:10.244926Z","signature_b64":"oIHhewd0fYmayrZZeFzak/nVnT227tTrp15/yTJrIU59VQQc83ILs21ZiSzGqXhPSOqyZl3NGSqkOsuu6AcRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"493b5de63ca909dd6369ca6d1bcf2beec2d1a147c2ababbf6b376b2e95b210e0","last_reissued_at":"2026-07-05T10:46:10.244411Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:10.244411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiyuan Li, Le Dai, Mingbang Wang, Ruilin Nong, Songhang Deng, Xingzu Liu, Zhang Dong","submitted_at":"2025-04-08T09:06:52Z","abstract_excerpt":"Large-scale coreference resolution presents a significant challenge in natural language processing, necessitating a balance between efficiency and accuracy. In response to this challenge, we introduce an End-to-End Neural Coreference Resolution system tailored for large-scale applications. Our system efficiently identifies and resolves coreference links in text, ensuring minimal computational overhead without compromising on performance. By utilizing advanced neural network architectures, we incorporate various contextual embeddings and attention mechanisms, which enhance the quality of predic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05824","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/2504.05824/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":"2504.05824","created_at":"2026-07-05T10:46:10.244481+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.05824v1","created_at":"2026-07-05T10:46:10.244481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05824","created_at":"2026-07-05T10:46:10.244481+00:00"},{"alias_kind":"pith_short_12","alias_value":"JE5V3ZR4VEE5","created_at":"2026-07-05T10:46:10.244481+00:00"},{"alias_kind":"pith_short_16","alias_value":"JE5V3ZR4VEE52Y3J","created_at":"2026-07-05T10:46:10.244481+00:00"},{"alias_kind":"pith_short_8","alias_value":"JE5V3ZR4","created_at":"2026-07-05T10:46:10.244481+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/JE5V3ZR4VEE52Y3JZJWRXTZL53","json":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53.json","graph_json":"https://pith.science/api/pith-number/JE5V3ZR4VEE52Y3JZJWRXTZL53/graph.json","events_json":"https://pith.science/api/pith-number/JE5V3ZR4VEE52Y3JZJWRXTZL53/events.json","paper":"https://pith.science/paper/JE5V3ZR4"},"agent_actions":{"view_html":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53","download_json":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53.json","view_paper":"https://pith.science/paper/JE5V3ZR4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.05824&json=true","fetch_graph":"https://pith.science/api/pith-number/JE5V3ZR4VEE52Y3JZJWRXTZL53/graph.json","fetch_events":"https://pith.science/api/pith-number/JE5V3ZR4VEE52Y3JZJWRXTZL53/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53/action/storage_attestation","attest_author":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53/action/author_attestation","sign_citation":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53/action/citation_signature","submit_replication":"https://pith.science/pith/JE5V3ZR4VEE52Y3JZJWRXTZL53/action/replication_record"}},"created_at":"2026-07-05T10:46:10.244481+00:00","updated_at":"2026-07-05T10:46:10.244481+00:00"}