{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:6LEPZ7KDK2R4CQOOFZLQEC23XH","short_pith_number":"pith:6LEPZ7KD","schema_version":"1.0","canonical_sha256":"f2c8fcfd4356a3c141ce2e57020b5bb9d2ec4ec6daadb10ca1cfb99ee4a76641","source":{"kind":"arxiv","id":"1901.00603","version":2},"attestation_state":"computed","paper":{"title":"Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Caiming Xiong, Nitish Shirish Keskar, Richard Socher, Victor Zhong","submitted_at":"2019-01-03T03:55:49Z","abstract_excerpt":"End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurr"},"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":"1901.00603","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-01-03T03:55:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"38bd8462ce5a1c5204e45cb63854ea12f1a7338e48d2bbf2cab68c0c3bd2ecf1","abstract_canon_sha256":"d96d221faef98f56ea2fd3066d25365e24d406067f1a727d49374065871aa90c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:29.211268Z","signature_b64":"cVyTTEW+OvQ0IxxnY5idJVd8VmCvNOXiP40BSpv9z97UrWbnKuB5WatG5Ww/YIcUc5iUNDjyWLtalBm+D9fOCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2c8fcfd4356a3c141ce2e57020b5bb9d2ec4ec6daadb10ca1cfb99ee4a76641","last_reissued_at":"2026-05-17T23:46:29.210629Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:29.210629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Caiming Xiong, Nitish Shirish Keskar, Richard Socher, Victor Zhong","submitted_at":"2019-01-03T03:55:49Z","abstract_excerpt":"End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00603","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":""},"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":"1901.00603","created_at":"2026-05-17T23:46:29.210728+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.00603v2","created_at":"2026-05-17T23:46:29.210728+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00603","created_at":"2026-05-17T23:46:29.210728+00:00"},{"alias_kind":"pith_short_12","alias_value":"6LEPZ7KDK2R4","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"6LEPZ7KDK2R4CQOO","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"6LEPZ7KD","created_at":"2026-05-18T12:33:10.108867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.14072","citing_title":"The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims","ref_index":149,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH","json":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH.json","graph_json":"https://pith.science/api/pith-number/6LEPZ7KDK2R4CQOOFZLQEC23XH/graph.json","events_json":"https://pith.science/api/pith-number/6LEPZ7KDK2R4CQOOFZLQEC23XH/events.json","paper":"https://pith.science/paper/6LEPZ7KD"},"agent_actions":{"view_html":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH","download_json":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH.json","view_paper":"https://pith.science/paper/6LEPZ7KD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.00603&json=true","fetch_graph":"https://pith.science/api/pith-number/6LEPZ7KDK2R4CQOOFZLQEC23XH/graph.json","fetch_events":"https://pith.science/api/pith-number/6LEPZ7KDK2R4CQOOFZLQEC23XH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH/action/storage_attestation","attest_author":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH/action/author_attestation","sign_citation":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH/action/citation_signature","submit_replication":"https://pith.science/pith/6LEPZ7KDK2R4CQOOFZLQEC23XH/action/replication_record"}},"created_at":"2026-05-17T23:46:29.210728+00:00","updated_at":"2026-05-17T23:46:29.210728+00:00"}