{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EB52LC2CRE3ZN2T62LFYBE57EV","short_pith_number":"pith:EB52LC2C","canonical_record":{"source":{"id":"1907.01643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-01T17:48:40Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"d76f17198948f509418a311ea02df14d174e291c1e873264a766958db08b01c2","abstract_canon_sha256":"fb370469cca933befd1b19b9d305bf0df9f36b7a1b4e8a8bf529cdf26ebabd92"},"schema_version":"1.0"},"canonical_sha256":"207ba58b42893796ea7ed2cb8093bf25531fbe379e9e555f43a995d3f387c3e6","source":{"kind":"arxiv","id":"1907.01643","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01643","created_at":"2026-05-17T23:41:35Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01643v1","created_at":"2026-05-17T23:41:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01643","created_at":"2026-05-17T23:41:35Z"},{"alias_kind":"pith_short_12","alias_value":"EB52LC2CRE3Z","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EB52LC2CRE3ZN2T6","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EB52LC2C","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EB52LC2CRE3ZN2T62LFYBE57EV","target":"record","payload":{"canonical_record":{"source":{"id":"1907.01643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-01T17:48:40Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"d76f17198948f509418a311ea02df14d174e291c1e873264a766958db08b01c2","abstract_canon_sha256":"fb370469cca933befd1b19b9d305bf0df9f36b7a1b4e8a8bf529cdf26ebabd92"},"schema_version":"1.0"},"canonical_sha256":"207ba58b42893796ea7ed2cb8093bf25531fbe379e9e555f43a995d3f387c3e6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:35.640373Z","signature_b64":"0ZveMWL4XnA0ueTloCHa8MZ0ShHssLmaXApx0ZdbT32r6mADWy2qKgEJIFx6NTTeYtefAlora42N+rKYYYDaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"207ba58b42893796ea7ed2cb8093bf25531fbe379e9e555f43a995d3f387c3e6","last_reissued_at":"2026-05-17T23:41:35.639705Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:35.639705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.01643","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":"2026-05-17T23:41:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hW/fpJV80ixpWK+pe/ZUGZvflc4IjRDvjuf++s947vBg5wNdaLDrw2Ghv2lxfYWFPzujJaIe+a0wA6UDfWIxCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T15:22:18.091390Z"},"content_sha256":"3f6692e6a42da499200e450f672cdd307db6bd36f0959e02bbb47e3eeb280976","schema_version":"1.0","event_id":"sha256:3f6692e6a42da499200e450f672cdd307db6bd36f0959e02bbb47e3eeb280976"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EB52LC2CRE3ZN2T62LFYBE57EV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Eric Nyberg, Hemant Pugaliya, Karan Saxena, Prashant Gupta, Sheetal Shalini, Shefali Garg, Teruko Mitamura","submitted_at":"2019-07-01T17:48:40Z","abstract_excerpt":"Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets . However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01643","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":"2026-05-17T23:41:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n4FycX5qiafoM0Mf3FM+Lsf0qgu455W43ikvnfk5Vu87BQGS3nu3qdTkO9hXM+Ov83WF0pfk/J4eDwQOZdwPBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T15:22:18.092055Z"},"content_sha256":"5a604bfb1786fea6dbdc20c5ca96f6e6ceedc3de8f4467623ee9e3f3834ab143","schema_version":"1.0","event_id":"sha256:5a604bfb1786fea6dbdc20c5ca96f6e6ceedc3de8f4467623ee9e3f3834ab143"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EB52LC2CRE3ZN2T62LFYBE57EV/bundle.json","state_url":"https://pith.science/pith/EB52LC2CRE3ZN2T62LFYBE57EV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EB52LC2CRE3ZN2T62LFYBE57EV/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":"2026-05-28T15:22:18Z","links":{"resolver":"https://pith.science/pith/EB52LC2CRE3ZN2T62LFYBE57EV","bundle":"https://pith.science/pith/EB52LC2CRE3ZN2T62LFYBE57EV/bundle.json","state":"https://pith.science/pith/EB52LC2CRE3ZN2T62LFYBE57EV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EB52LC2CRE3ZN2T62LFYBE57EV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EB52LC2CRE3ZN2T62LFYBE57EV","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":"fb370469cca933befd1b19b9d305bf0df9f36b7a1b4e8a8bf529cdf26ebabd92","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-01T17:48:40Z","title_canon_sha256":"d76f17198948f509418a311ea02df14d174e291c1e873264a766958db08b01c2"},"schema_version":"1.0","source":{"id":"1907.01643","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01643","created_at":"2026-05-17T23:41:35Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01643v1","created_at":"2026-05-17T23:41:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01643","created_at":"2026-05-17T23:41:35Z"},{"alias_kind":"pith_short_12","alias_value":"EB52LC2CRE3Z","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EB52LC2CRE3ZN2T6","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EB52LC2C","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:5a604bfb1786fea6dbdc20c5ca96f6e6ceedc3de8f4467623ee9e3f3834ab143","target":"graph","created_at":"2026-05-17T23:41:35Z","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":"Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets . However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In","authors_text":"Eric Nyberg, Hemant Pugaliya, Karan Saxena, Prashant Gupta, Sheetal Shalini, Shefali Garg, Teruko Mitamura","cross_cats":["cs.CL","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-01T17:48:40Z","title":"Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01643","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":"sha256:3f6692e6a42da499200e450f672cdd307db6bd36f0959e02bbb47e3eeb280976","target":"record","created_at":"2026-05-17T23:41:35Z","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":"fb370469cca933befd1b19b9d305bf0df9f36b7a1b4e8a8bf529cdf26ebabd92","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-01T17:48:40Z","title_canon_sha256":"d76f17198948f509418a311ea02df14d174e291c1e873264a766958db08b01c2"},"schema_version":"1.0","source":{"id":"1907.01643","kind":"arxiv","version":1}},"canonical_sha256":"207ba58b42893796ea7ed2cb8093bf25531fbe379e9e555f43a995d3f387c3e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"207ba58b42893796ea7ed2cb8093bf25531fbe379e9e555f43a995d3f387c3e6","first_computed_at":"2026-05-17T23:41:35.639705Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:35.639705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0ZveMWL4XnA0ueTloCHa8MZ0ShHssLmaXApx0ZdbT32r6mADWy2qKgEJIFx6NTTeYtefAlora42N+rKYYYDaBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:35.640373Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.01643","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f6692e6a42da499200e450f672cdd307db6bd36f0959e02bbb47e3eeb280976","sha256:5a604bfb1786fea6dbdc20c5ca96f6e6ceedc3de8f4467623ee9e3f3834ab143"],"state_sha256":"57561139edc4ff18dee1eb9dce019cda2f30d89749575a9a6d3e768ad73654ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D8kYv+66FFRrqADsJcsgXXxo1gnNOGwC+OWmZ3NUL9RirzBxgGACRmXlSNMfoiSctM7WzUKsiDEwCo7qGriFBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T15:22:18.095838Z","bundle_sha256":"be0afadf3d9a364af7a5d4e16e844e1c97baf53be1860fdb68b08bb874a7da3c"}}