{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QAQ4PYZMBTJ4QBWNEZ6J6XJKRS","short_pith_number":"pith:QAQ4PYZM","canonical_record":{"source":{"id":"1909.02209","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-05T04:47:10Z","cross_cats_sorted":[],"title_canon_sha256":"b1b216f2d28762b7274a7dacfc7bf1b26f4234e9f9499146c9cd4a735dbb2351","abstract_canon_sha256":"4c15365e040feb707ebeaac42dce70c8135c34065fad4585f74ee1d5618abf32"},"schema_version":"1.0"},"canonical_sha256":"8021c7e32c0cd3c806cd267c9f5d2a8c97ac8381f3eb451a8a597fb731fe3a7f","source":{"kind":"arxiv","id":"1909.02209","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.02209","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"arxiv_version","alias_value":"1909.02209v3","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02209","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"pith_short_12","alias_value":"QAQ4PYZMBTJ4","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"pith_short_16","alias_value":"QAQ4PYZMBTJ4QBWN","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"pith_short_8","alias_value":"QAQ4PYZM","created_at":"2026-07-05T00:38:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QAQ4PYZMBTJ4QBWNEZ6J6XJKRS","target":"record","payload":{"canonical_record":{"source":{"id":"1909.02209","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-05T04:47:10Z","cross_cats_sorted":[],"title_canon_sha256":"b1b216f2d28762b7274a7dacfc7bf1b26f4234e9f9499146c9cd4a735dbb2351","abstract_canon_sha256":"4c15365e040feb707ebeaac42dce70c8135c34065fad4585f74ee1d5618abf32"},"schema_version":"1.0"},"canonical_sha256":"8021c7e32c0cd3c806cd267c9f5d2a8c97ac8381f3eb451a8a597fb731fe3a7f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:38:04.200537Z","signature_b64":"VopQvODT0EZoAas9CMtP28YTLvkXreVKcLGdUqf65NL6esB5uijC33tZTIDSOvUf9Jf6Taix5slTqi5SazHNBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8021c7e32c0cd3c806cd267c9f5d2a8c97ac8381f3eb451a8a597fb731fe3a7f","last_reissued_at":"2026-07-05T00:38:04.200108Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:38:04.200108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.02209","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:38:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/jRfeodGQJpECfW3Uv78d2T0yPK6t9N9baSkYOY+p1KQCDb70i8jdvO8w0idgKHuT6uutZPeIpGUcmngENU6CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:05:17.469642Z"},"content_sha256":"aeff0390d0d85221a6f3cc2c8e32ab826b8c98941e34212785fd6342b3253044","schema_version":"1.0","event_id":"sha256:aeff0390d0d85221a6f3cc2c8e32ab826b8c98941e34212785fd6342b3253044"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QAQ4PYZMBTJ4QBWNEZ6J6XJKRS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semantics-aware BERT for Language Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hai Zhao, Shuailiang Zhang, Xiang Zhou, Xi Zhou, Yuwei Wu, Zhuosheng Zhang, Zuchao Li","submitted_at":"2019-09-05T04:47:10Z","abstract_excerpt":"The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02209","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.02209/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":"2026-07-05T00:38:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ujDu8ZxsVJu4l0lv3gDYADRkfwrpDonBWTP0LgrSJ394sxvalBcKXGHgWb7AMtLacgu59WtDdCodSOywr8AIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:05:17.470174Z"},"content_sha256":"36df545fe822f9cfdb45c7484d33e7921359d8831c1f016e1a637517e7f9dddc","schema_version":"1.0","event_id":"sha256:36df545fe822f9cfdb45c7484d33e7921359d8831c1f016e1a637517e7f9dddc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS/bundle.json","state_url":"https://pith.science/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS/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":"2026-08-15T18:05:17Z","links":{"resolver":"https://pith.science/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS","bundle":"https://pith.science/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS/bundle.json","state":"https://pith.science/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QAQ4PYZMBTJ4QBWNEZ6J6XJKRS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QAQ4PYZMBTJ4QBWNEZ6J6XJKRS","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":"4c15365e040feb707ebeaac42dce70c8135c34065fad4585f74ee1d5618abf32","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-05T04:47:10Z","title_canon_sha256":"b1b216f2d28762b7274a7dacfc7bf1b26f4234e9f9499146c9cd4a735dbb2351"},"schema_version":"1.0","source":{"id":"1909.02209","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.02209","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"arxiv_version","alias_value":"1909.02209v3","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02209","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"pith_short_12","alias_value":"QAQ4PYZMBTJ4","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"pith_short_16","alias_value":"QAQ4PYZMBTJ4QBWN","created_at":"2026-07-05T00:38:04Z"},{"alias_kind":"pith_short_8","alias_value":"QAQ4PYZM","created_at":"2026-07-05T00:38:04Z"}],"graph_snapshots":[{"event_id":"sha256:36df545fe822f9cfdb45c7484d33e7921359d8831c1f016e1a637517e7f9dddc","target":"graph","created_at":"2026-07-05T00:38:04Z","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/1909.02209/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incor","authors_text":"Hai Zhao, Shuailiang Zhang, Xiang Zhou, Xi Zhou, Yuwei Wu, Zhuosheng Zhang, Zuchao Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-05T04:47:10Z","title":"Semantics-aware BERT for Language Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02209","kind":"arxiv","version":3},"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:aeff0390d0d85221a6f3cc2c8e32ab826b8c98941e34212785fd6342b3253044","target":"record","created_at":"2026-07-05T00:38:04Z","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":"4c15365e040feb707ebeaac42dce70c8135c34065fad4585f74ee1d5618abf32","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-05T04:47:10Z","title_canon_sha256":"b1b216f2d28762b7274a7dacfc7bf1b26f4234e9f9499146c9cd4a735dbb2351"},"schema_version":"1.0","source":{"id":"1909.02209","kind":"arxiv","version":3}},"canonical_sha256":"8021c7e32c0cd3c806cd267c9f5d2a8c97ac8381f3eb451a8a597fb731fe3a7f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8021c7e32c0cd3c806cd267c9f5d2a8c97ac8381f3eb451a8a597fb731fe3a7f","first_computed_at":"2026-07-05T00:38:04.200108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:38:04.200108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VopQvODT0EZoAas9CMtP28YTLvkXreVKcLGdUqf65NL6esB5uijC33tZTIDSOvUf9Jf6Taix5slTqi5SazHNBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:38:04.200537Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.02209","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aeff0390d0d85221a6f3cc2c8e32ab826b8c98941e34212785fd6342b3253044","sha256:36df545fe822f9cfdb45c7484d33e7921359d8831c1f016e1a637517e7f9dddc"],"state_sha256":"ee17a5ec019c7df8996df7e67931a81a3ca3a859fab9ce9193a08204b8714b33"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eo2uf/39edHVNVVlDAFPXXnbjYzSGvIV9Zqj6yhETthJuVQ1YiRxdImSFbmea8ec1RYHyQOy3aOEy9NX163ZBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T18:05:17.475588Z","bundle_sha256":"202147c566c41c34a18f83c471e696201925d0e0bcd8c0ff45c40b95bdb41036"}}