{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:5NX5J6SHR2K6AAW22A3GWIYX4M","short_pith_number":"pith:5NX5J6SH","canonical_record":{"source":{"id":"1908.04942","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-14T03:40:04Z","cross_cats_sorted":[],"title_canon_sha256":"e0021ea667bfadc4c7fdfa6042cac8771f2a90a4f01a17e0957042b9feedaa71","abstract_canon_sha256":"794f3351585676e89b861bd28860614ca5ae5f0fc1ac53653ad56b83f1056f91"},"schema_version":"1.0"},"canonical_sha256":"eb6fd4fa478e95e002dad0366b2317e300a8219ba0cb8c97e78460ff6210911b","source":{"kind":"arxiv","id":"1908.04942","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.04942","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"arxiv_version","alias_value":"1908.04942v4","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.04942","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"pith_short_12","alias_value":"5NX5J6SHR2K6","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"5NX5J6SHR2K6AAW2","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"5NX5J6SH","created_at":"2026-07-05T01:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:5NX5J6SHR2K6AAW22A3GWIYX4M","target":"record","payload":{"canonical_record":{"source":{"id":"1908.04942","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-14T03:40:04Z","cross_cats_sorted":[],"title_canon_sha256":"e0021ea667bfadc4c7fdfa6042cac8771f2a90a4f01a17e0957042b9feedaa71","abstract_canon_sha256":"794f3351585676e89b861bd28860614ca5ae5f0fc1ac53653ad56b83f1056f91"},"schema_version":"1.0"},"canonical_sha256":"eb6fd4fa478e95e002dad0366b2317e300a8219ba0cb8c97e78460ff6210911b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:30:48.842356Z","signature_b64":"3fQV/AT9rvcQnahXQeQ1Yt03yhsi/03RZ5qzQsy7+Hf9pXYQJWviNFuEMwsjSo6wMs1fRZ39nwL3pgtgo79/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb6fd4fa478e95e002dad0366b2317e300a8219ba0cb8c97e78460ff6210911b","last_reissued_at":"2026-07-05T01:30:48.841872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:30:48.841872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.04942","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:30:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7JhtLZTAiZZjESXUXN/T5bSniNigb5Zra1hAZITZ0QJh8/l+jC07sewtZ3Dx5ySEOokJWCquyPWnhQ//PuP6Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T18:23:58.198006Z"},"content_sha256":"bbdfca565a87d9542291c984c813e72a411c73a5bdf788122c04dfadcebc5ae1","schema_version":"1.0","event_id":"sha256:bbdfca565a87d9542291c984c813e72a411c73a5bdf788122c04dfadcebc5ae1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:5NX5J6SHR2K6AAW22A3GWIYX4M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Lingfei Wu, Mohammed J. Zaki, Yu Chen","submitted_at":"2019-08-14T03:40:04Z","abstract_excerpt":"Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail to fully exploit the answer information. To address these limitations, in this paper, we propose a reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator with a novel Bidirectional Gated Graph Neural Network based "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.04942","kind":"arxiv","version":4},"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/1908.04942/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:30:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8tBxvgAqcyTk0JZoGqs1MmfFeFg9aGwf2m9l8IYAlVyj9dsSdEZum2vnLFnm+vgdkIY4yUoUi7D82GS3RGJoBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T18:23:58.198663Z"},"content_sha256":"d5d17110a8ffe557544ee8b012d92206c53dbd50df8c1ac4649d6aa801445ad6","schema_version":"1.0","event_id":"sha256:d5d17110a8ffe557544ee8b012d92206c53dbd50df8c1ac4649d6aa801445ad6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5NX5J6SHR2K6AAW22A3GWIYX4M/bundle.json","state_url":"https://pith.science/pith/5NX5J6SHR2K6AAW22A3GWIYX4M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5NX5J6SHR2K6AAW22A3GWIYX4M/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":"2026-08-19T18:23:58Z","links":{"resolver":"https://pith.science/pith/5NX5J6SHR2K6AAW22A3GWIYX4M","bundle":"https://pith.science/pith/5NX5J6SHR2K6AAW22A3GWIYX4M/bundle.json","state":"https://pith.science/pith/5NX5J6SHR2K6AAW22A3GWIYX4M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5NX5J6SHR2K6AAW22A3GWIYX4M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:5NX5J6SHR2K6AAW22A3GWIYX4M","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":"794f3351585676e89b861bd28860614ca5ae5f0fc1ac53653ad56b83f1056f91","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-14T03:40:04Z","title_canon_sha256":"e0021ea667bfadc4c7fdfa6042cac8771f2a90a4f01a17e0957042b9feedaa71"},"schema_version":"1.0","source":{"id":"1908.04942","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.04942","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"arxiv_version","alias_value":"1908.04942v4","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.04942","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"pith_short_12","alias_value":"5NX5J6SHR2K6","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"5NX5J6SHR2K6AAW2","created_at":"2026-07-05T01:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"5NX5J6SH","created_at":"2026-07-05T01:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:d5d17110a8ffe557544ee8b012d92206c53dbd50df8c1ac4649d6aa801445ad6","target":"graph","created_at":"2026-07-05T01:30:48Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.04942/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail to fully exploit the answer information. To address these limitations, in this paper, we propose a reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator with a novel Bidirectional Gated Graph Neural Network based ","authors_text":"Lingfei Wu, Mohammed J. Zaki, Yu Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-14T03:40:04Z","title":"Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.04942","kind":"arxiv","version":4},"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:bbdfca565a87d9542291c984c813e72a411c73a5bdf788122c04dfadcebc5ae1","target":"record","created_at":"2026-07-05T01:30:48Z","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":"794f3351585676e89b861bd28860614ca5ae5f0fc1ac53653ad56b83f1056f91","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-14T03:40:04Z","title_canon_sha256":"e0021ea667bfadc4c7fdfa6042cac8771f2a90a4f01a17e0957042b9feedaa71"},"schema_version":"1.0","source":{"id":"1908.04942","kind":"arxiv","version":4}},"canonical_sha256":"eb6fd4fa478e95e002dad0366b2317e300a8219ba0cb8c97e78460ff6210911b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb6fd4fa478e95e002dad0366b2317e300a8219ba0cb8c97e78460ff6210911b","first_computed_at":"2026-07-05T01:30:48.841872Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:30:48.841872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3fQV/AT9rvcQnahXQeQ1Yt03yhsi/03RZ5qzQsy7+Hf9pXYQJWviNFuEMwsjSo6wMs1fRZ39nwL3pgtgo79/CA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:30:48.842356Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.04942","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bbdfca565a87d9542291c984c813e72a411c73a5bdf788122c04dfadcebc5ae1","sha256:d5d17110a8ffe557544ee8b012d92206c53dbd50df8c1ac4649d6aa801445ad6"],"state_sha256":"56fe59049d7bdfaf384057b7427d7dc58c603e506a4fb50f44587a35e65bca90"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A9VH2EMl4aydbwev4ov7D8oFNi9PNZqtCAsXY7MhA+ZYV7L/v617BlGcqK2B1J3A4qQA2q9Tip85BKpptnqfAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T18:23:58.203855Z","bundle_sha256":"093ca74b9451ae3fb3cbae9cd96928c13bf62e758e7a5f7bcbaced4a55658712"}}