{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JEVXU7U2EH7W72P6ZMADJ5NQG2","short_pith_number":"pith:JEVXU7U2","schema_version":"1.0","canonical_sha256":"492b7a7e9a21ff6fe9fecb0034f5b03690db17ed76d4ac1a6f07463854a0f0eb","source":{"kind":"arxiv","id":"1703.04816","version":3},"attestation_state":"computed","paper":{"title":"Making Neural QA as Simple as Possible but not Simpler","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.CL","authors_text":"Dirk Weissenborn, Georg Wiese, Laura Seiffe","submitted_at":"2017-03-14T23:09:45Z","abstract_excerpt":"Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, "},"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":"1703.04816","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-03-14T23:09:45Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"09a70cbbffa31dc78ef8f94896ca2100437f0cbea6418cb7223d786cc0982d57","abstract_canon_sha256":"573dce21d2c3563610a996cc1191ecdbcb7a25bed9ad228775d6522b414ccf5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:46.072456Z","signature_b64":"NCcwag2cj+Xcm1R0xPq6TYCdRnsxKs1kjDyRhTCZuIWP4i344QSPzbxXM9RAoYF3ducDEGxc9eFE7Ds/cBBkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"492b7a7e9a21ff6fe9fecb0034f5b03690db17ed76d4ac1a6f07463854a0f0eb","last_reissued_at":"2026-05-18T00:42:46.071848Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:46.071848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Making Neural QA as Simple as Possible but not Simpler","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.CL","authors_text":"Dirk Weissenborn, Georg Wiese, Laura Seiffe","submitted_at":"2017-03-14T23:09:45Z","abstract_excerpt":"Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.04816","kind":"arxiv","version":3},"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":"1703.04816","created_at":"2026-05-18T00:42:46.071939+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.04816v3","created_at":"2026-05-18T00:42:46.071939+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.04816","created_at":"2026-05-18T00:42:46.071939+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEVXU7U2EH7W","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEVXU7U2EH7W72P6","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEVXU7U2","created_at":"2026-05-18T12:31:24.725408+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.01686","citing_title":"Machine Reading Comprehension: a Literature Review","ref_index":62,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2","json":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2.json","graph_json":"https://pith.science/api/pith-number/JEVXU7U2EH7W72P6ZMADJ5NQG2/graph.json","events_json":"https://pith.science/api/pith-number/JEVXU7U2EH7W72P6ZMADJ5NQG2/events.json","paper":"https://pith.science/paper/JEVXU7U2"},"agent_actions":{"view_html":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2","download_json":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2.json","view_paper":"https://pith.science/paper/JEVXU7U2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.04816&json=true","fetch_graph":"https://pith.science/api/pith-number/JEVXU7U2EH7W72P6ZMADJ5NQG2/graph.json","fetch_events":"https://pith.science/api/pith-number/JEVXU7U2EH7W72P6ZMADJ5NQG2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2/action/storage_attestation","attest_author":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2/action/author_attestation","sign_citation":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2/action/citation_signature","submit_replication":"https://pith.science/pith/JEVXU7U2EH7W72P6ZMADJ5NQG2/action/replication_record"}},"created_at":"2026-05-18T00:42:46.071939+00:00","updated_at":"2026-05-18T00:42:46.071939+00:00"}