{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:N35JVD6FP555ZR3JUNHVOJALYG","short_pith_number":"pith:N35JVD6F","canonical_record":{"source":{"id":"1906.03824","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-10T07:49:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"13813b069b99e94aefa2563428d5a79690df9b98cbc6fcee9657bd387c3352c4","abstract_canon_sha256":"24065a5f37516c695f65e3310a8cee9cb767069ac4fd0ddc5b6eceefbab931df"},"schema_version":"1.0"},"canonical_sha256":"6efa9a8fc57f7bdcc769a34f57240bc1b1320735d5f532c28d7ae8a430623d5c","source":{"kind":"arxiv","id":"1906.03824","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03824","created_at":"2026-05-17T23:43:44Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03824v1","created_at":"2026-05-17T23:43:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03824","created_at":"2026-05-17T23:43:44Z"},{"alias_kind":"pith_short_12","alias_value":"N35JVD6FP555","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"N35JVD6FP555ZR3J","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"N35JVD6F","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:N35JVD6FP555ZR3JUNHVOJALYG","target":"record","payload":{"canonical_record":{"source":{"id":"1906.03824","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-10T07:49:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"13813b069b99e94aefa2563428d5a79690df9b98cbc6fcee9657bd387c3352c4","abstract_canon_sha256":"24065a5f37516c695f65e3310a8cee9cb767069ac4fd0ddc5b6eceefbab931df"},"schema_version":"1.0"},"canonical_sha256":"6efa9a8fc57f7bdcc769a34f57240bc1b1320735d5f532c28d7ae8a430623d5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:44.842149Z","signature_b64":"dS7VTdNasEgSvifA1g8uOjyRr3E1H6JXuyaLQqKJ/ccpE2K6an67vAr8QPkEKc7EAOgFDI4FoVgTh4rJ43CABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6efa9a8fc57f7bdcc769a34f57240bc1b1320735d5f532c28d7ae8a430623d5c","last_reissued_at":"2026-05-17T23:43:44.841514Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:44.841514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.03824","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Txw9xO5pSv+0muu6V2SudUom4O2b8G4AGct40znsy+rFWE9t+LUI+xxmkKpdrs9kn/xlO6/oxzISgYPAE5xdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T23:12:42.645442Z"},"content_sha256":"678e777e8cb0ef0f61a5ce3bb5e36d9135b018c7e61083d721a443575fbcd6d4","schema_version":"1.0","event_id":"sha256:678e777e8cb0ef0f61a5ce3bb5e36d9135b018c7e61083d721a443575fbcd6d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:N35JVD6FP555ZR3JUNHVOJALYG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Neural Machine Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Boyu Qiu, Jungang Xu, Xu Chen, Yingfei Sun","submitted_at":"2019-06-10T07:49:14Z","abstract_excerpt":"Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. In recent years, the popularity of deep learning and the establishment of large-scale datasets have both promoted the prosperity of Machine Reading Comprehension. This paper aims to present how to utilize the Neural Network to build a Reader and introduce some classic models, analyze what improvements they make. Further, we also point out the defects of existing models and future research directions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03824","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZXA45JoWvH5sALtxRcQmaCJvMQaRJT80gClhLA4AHOmeiyIoP9xgMtuoE/J0ypPRKLB+vDczDl4RAu/ItPcmAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T23:12:42.645784Z"},"content_sha256":"2c5c4dbb2cb39d3f3f412641008656ec28721d814eb4dd8f5ac08bcc0fefa917","schema_version":"1.0","event_id":"sha256:2c5c4dbb2cb39d3f3f412641008656ec28721d814eb4dd8f5ac08bcc0fefa917"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N35JVD6FP555ZR3JUNHVOJALYG/bundle.json","state_url":"https://pith.science/pith/N35JVD6FP555ZR3JUNHVOJALYG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N35JVD6FP555ZR3JUNHVOJALYG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-04T23:12:42Z","links":{"resolver":"https://pith.science/pith/N35JVD6FP555ZR3JUNHVOJALYG","bundle":"https://pith.science/pith/N35JVD6FP555ZR3JUNHVOJALYG/bundle.json","state":"https://pith.science/pith/N35JVD6FP555ZR3JUNHVOJALYG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N35JVD6FP555ZR3JUNHVOJALYG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:N35JVD6FP555ZR3JUNHVOJALYG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"24065a5f37516c695f65e3310a8cee9cb767069ac4fd0ddc5b6eceefbab931df","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-10T07:49:14Z","title_canon_sha256":"13813b069b99e94aefa2563428d5a79690df9b98cbc6fcee9657bd387c3352c4"},"schema_version":"1.0","source":{"id":"1906.03824","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03824","created_at":"2026-05-17T23:43:44Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03824v1","created_at":"2026-05-17T23:43:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03824","created_at":"2026-05-17T23:43:44Z"},{"alias_kind":"pith_short_12","alias_value":"N35JVD6FP555","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"N35JVD6FP555ZR3J","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"N35JVD6F","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:2c5c4dbb2cb39d3f3f412641008656ec28721d814eb4dd8f5ac08bcc0fefa917","target":"graph","created_at":"2026-05-17T23:43:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. In recent years, the popularity of deep learning and the establishment of large-scale datasets have both promoted the prosperity of Machine Reading Comprehension. This paper aims to present how to utilize the Neural Network to build a Reader and introduce some classic models, analyze what improvements they make. Further, we also point out the defects of existing models and future research directions","authors_text":"Boyu Qiu, Jungang Xu, Xu Chen, Yingfei Sun","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-10T07:49:14Z","title":"A Survey on Neural Machine Reading Comprehension"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03824","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:678e777e8cb0ef0f61a5ce3bb5e36d9135b018c7e61083d721a443575fbcd6d4","target":"record","created_at":"2026-05-17T23:43:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"24065a5f37516c695f65e3310a8cee9cb767069ac4fd0ddc5b6eceefbab931df","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-10T07:49:14Z","title_canon_sha256":"13813b069b99e94aefa2563428d5a79690df9b98cbc6fcee9657bd387c3352c4"},"schema_version":"1.0","source":{"id":"1906.03824","kind":"arxiv","version":1}},"canonical_sha256":"6efa9a8fc57f7bdcc769a34f57240bc1b1320735d5f532c28d7ae8a430623d5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6efa9a8fc57f7bdcc769a34f57240bc1b1320735d5f532c28d7ae8a430623d5c","first_computed_at":"2026-05-17T23:43:44.841514Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:44.841514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dS7VTdNasEgSvifA1g8uOjyRr3E1H6JXuyaLQqKJ/ccpE2K6an67vAr8QPkEKc7EAOgFDI4FoVgTh4rJ43CABQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:44.842149Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.03824","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:678e777e8cb0ef0f61a5ce3bb5e36d9135b018c7e61083d721a443575fbcd6d4","sha256:2c5c4dbb2cb39d3f3f412641008656ec28721d814eb4dd8f5ac08bcc0fefa917"],"state_sha256":"b4dff1cdf3a9e6f01a515e8cd8a6a08fe1bae1e3e11e734cd4a23686dd868467"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PwHkCJFp+MLd9GFKzVbbp6rzisWSQV4FM0l09O4bzVUCF1f6zs2JqQ6GUoGBWpWVFx3ANE14J1ZkOgqnmtfsBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T23:12:42.647684Z","bundle_sha256":"07301952e4d356dab1767ef54e746123fc976ff3b58ebb835b7af8cb991b4513"}}