{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JIGWMN4MM2RGR4GVU6XSR4UBXX","short_pith_number":"pith:JIGWMN4M","schema_version":"1.0","canonical_sha256":"4a0d66378c66a268f0d5a7af28f281bdd23abcbe65b83e15270c48abf144ca24","source":{"kind":"arxiv","id":"1709.00023","version":2},"attestation_state":"computed","paper":{"title":"R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bowen Zhou, Gerald Tesauro, Jing Jiang, Mo Yu, Shiyu Chang, Shuohang Wang, Tim Klinger, Wei Zhang, Xiaoxiao Guo, Zhiguo Wang","submitted_at":"2017-08-31T18:08:35Z","abstract_excerpt":"In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al., 2017a). This setting is more comple"},"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":"1709.00023","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-31T18:08:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"691696c63e7ecb1a9b4ef8c38bcfb1c03c8ad4d8d98aa911c6e6e79bedb6d3d9","abstract_canon_sha256":"1b3bc7eeb8e1cefebdfed3bc85d1bdc1cf7afc184d2dc4315c67716754aec28d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:00.217412Z","signature_b64":"6rCUSV6H6EJQ3LrjB3F99sjvmmMU359FUoEgQ0XYchpQL9QhR2dVIcwIMHyMOsQFt8DoyAEdyH6jtSeV4HO8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a0d66378c66a268f0d5a7af28f281bdd23abcbe65b83e15270c48abf144ca24","last_reissued_at":"2026-05-18T00:30:00.216933Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:00.216933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bowen Zhou, Gerald Tesauro, Jing Jiang, Mo Yu, Shiyu Chang, Shuohang Wang, Tim Klinger, Wei Zhang, Xiaoxiao Guo, Zhiguo Wang","submitted_at":"2017-08-31T18:08:35Z","abstract_excerpt":"In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al., 2017a). This setting is more comple"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00023","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":"1709.00023","created_at":"2026-05-18T00:30:00.216999+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.00023v2","created_at":"2026-05-18T00:30:00.216999+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00023","created_at":"2026-05-18T00:30:00.216999+00:00"},{"alias_kind":"pith_short_12","alias_value":"JIGWMN4MM2RG","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"JIGWMN4MM2RGR4GV","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"JIGWMN4M","created_at":"2026-05-18T12:31:24.725408+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/JIGWMN4MM2RGR4GVU6XSR4UBXX","json":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX.json","graph_json":"https://pith.science/api/pith-number/JIGWMN4MM2RGR4GVU6XSR4UBXX/graph.json","events_json":"https://pith.science/api/pith-number/JIGWMN4MM2RGR4GVU6XSR4UBXX/events.json","paper":"https://pith.science/paper/JIGWMN4M"},"agent_actions":{"view_html":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX","download_json":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX.json","view_paper":"https://pith.science/paper/JIGWMN4M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.00023&json=true","fetch_graph":"https://pith.science/api/pith-number/JIGWMN4MM2RGR4GVU6XSR4UBXX/graph.json","fetch_events":"https://pith.science/api/pith-number/JIGWMN4MM2RGR4GVU6XSR4UBXX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX/action/storage_attestation","attest_author":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX/action/author_attestation","sign_citation":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX/action/citation_signature","submit_replication":"https://pith.science/pith/JIGWMN4MM2RGR4GVU6XSR4UBXX/action/replication_record"}},"created_at":"2026-05-18T00:30:00.216999+00:00","updated_at":"2026-05-18T00:30:00.216999+00:00"}