{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:S5TKTKM7TX7IPRSUJGGBTKS2D2","short_pith_number":"pith:S5TKTKM7","canonical_record":{"source":{"id":"1908.04577","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-13T11:12:58Z","cross_cats_sorted":[],"title_canon_sha256":"a859c24f91f1354713647ca1a277a0479fe4ef529bffd31a71a13d24eb6c4b4c","abstract_canon_sha256":"39a7357eae8870c03147ab7d0cc909cfb26de91444eb137f12b3efe568b12188"},"schema_version":"1.0"},"canonical_sha256":"9766a9a99f9dfe87c654498c19aa5a1eb1dc61c07293ce4c5f1a5b9fec3a845d","source":{"kind":"arxiv","id":"1908.04577","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.04577","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"arxiv_version","alias_value":"1908.04577v3","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.04577","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_12","alias_value":"S5TKTKM7TX7I","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_16","alias_value":"S5TKTKM7TX7IPRSU","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_8","alias_value":"S5TKTKM7","created_at":"2026-07-05T00:07:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:S5TKTKM7TX7IPRSUJGGBTKS2D2","target":"record","payload":{"canonical_record":{"source":{"id":"1908.04577","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-13T11:12:58Z","cross_cats_sorted":[],"title_canon_sha256":"a859c24f91f1354713647ca1a277a0479fe4ef529bffd31a71a13d24eb6c4b4c","abstract_canon_sha256":"39a7357eae8870c03147ab7d0cc909cfb26de91444eb137f12b3efe568b12188"},"schema_version":"1.0"},"canonical_sha256":"9766a9a99f9dfe87c654498c19aa5a1eb1dc61c07293ce4c5f1a5b9fec3a845d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:37.186092Z","signature_b64":"C9uSWW+K8sO1gy+iwjnJBxe7gL7+R5wY+c5hHzBxmD9ivOSLYFg4DIguw0b5G6e7MU2jLwFtAH5Zw2JQSTy3Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9766a9a99f9dfe87c654498c19aa5a1eb1dc61c07293ce4c5f1a5b9fec3a845d","last_reissued_at":"2026-07-05T00:07:37.185643Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:37.185643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.04577","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":"2026-07-05T00:07:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WaBjoxZKbO1RKQXOlqJG10CNeGyvmVy2UBuPKxwItfqEMEeTGhW7At2golHbyxjqiKsEj1rn+GxYFqM/3FlcDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:49:18.644681Z"},"content_sha256":"281c909e3ceb0abde73da055ad0571d1f623143fddf91602e514bb2e67194b68","schema_version":"1.0","event_id":"sha256:281c909e3ceb0abde73da055ad0571d1f623143fddf91602e514bb2e67194b68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:S5TKTKM7TX7IPRSUJGGBTKS2D2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Bi, Chen Wu, Jiangnan Xia, Liwei Peng, Luo Si, Ming Yan, Wei Wang, Zuyi Bao","submitted_at":"2019-08-13T11:12:58Z","abstract_excerpt":"Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman [8], we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.04577","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/1908.04577/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":"2026-07-05T00:07:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LYTaYPqjWx6wQoefVr0e8BdJgrZOWnMFb5ybcJPAWOZdfqw2mqjURpE4j83Bd/nN46jqo8XR3U8ZoW49qdruDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:49:18.645204Z"},"content_sha256":"200d477ffcc3d49faf57cf20d4a8abbb931b5d8a515251c80834442abed2bc43","schema_version":"1.0","event_id":"sha256:200d477ffcc3d49faf57cf20d4a8abbb931b5d8a515251c80834442abed2bc43"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2/bundle.json","state_url":"https://pith.science/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2/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":"2026-08-16T14:49:18Z","links":{"resolver":"https://pith.science/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2","bundle":"https://pith.science/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2/bundle.json","state":"https://pith.science/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S5TKTKM7TX7IPRSUJGGBTKS2D2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:S5TKTKM7TX7IPRSUJGGBTKS2D2","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":"39a7357eae8870c03147ab7d0cc909cfb26de91444eb137f12b3efe568b12188","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-13T11:12:58Z","title_canon_sha256":"a859c24f91f1354713647ca1a277a0479fe4ef529bffd31a71a13d24eb6c4b4c"},"schema_version":"1.0","source":{"id":"1908.04577","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.04577","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"arxiv_version","alias_value":"1908.04577v3","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.04577","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_12","alias_value":"S5TKTKM7TX7I","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_16","alias_value":"S5TKTKM7TX7IPRSU","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_8","alias_value":"S5TKTKM7","created_at":"2026-07-05T00:07:37Z"}],"graph_snapshots":[{"event_id":"sha256:200d477ffcc3d49faf57cf20d4a8abbb931b5d8a515251c80834442abed2bc43","target":"graph","created_at":"2026-07-05T00:07:37Z","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/1908.04577/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman [8], we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential ","authors_text":"Bin Bi, Chen Wu, Jiangnan Xia, Liwei Peng, Luo Si, Ming Yan, Wei Wang, Zuyi Bao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-13T11:12:58Z","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.04577","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":"sha256:281c909e3ceb0abde73da055ad0571d1f623143fddf91602e514bb2e67194b68","target":"record","created_at":"2026-07-05T00:07:37Z","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":"39a7357eae8870c03147ab7d0cc909cfb26de91444eb137f12b3efe568b12188","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-13T11:12:58Z","title_canon_sha256":"a859c24f91f1354713647ca1a277a0479fe4ef529bffd31a71a13d24eb6c4b4c"},"schema_version":"1.0","source":{"id":"1908.04577","kind":"arxiv","version":3}},"canonical_sha256":"9766a9a99f9dfe87c654498c19aa5a1eb1dc61c07293ce4c5f1a5b9fec3a845d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9766a9a99f9dfe87c654498c19aa5a1eb1dc61c07293ce4c5f1a5b9fec3a845d","first_computed_at":"2026-07-05T00:07:37.185643Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:07:37.185643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C9uSWW+K8sO1gy+iwjnJBxe7gL7+R5wY+c5hHzBxmD9ivOSLYFg4DIguw0b5G6e7MU2jLwFtAH5Zw2JQSTy3Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:07:37.186092Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.04577","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:281c909e3ceb0abde73da055ad0571d1f623143fddf91602e514bb2e67194b68","sha256:200d477ffcc3d49faf57cf20d4a8abbb931b5d8a515251c80834442abed2bc43"],"state_sha256":"710155267f4fd4498d33bd7d42daca9873a41baaea107bf06e3f65c4dcfcd0c4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kkAbIFg6bo0nb3Ehx4iGYUkjYnP4YT1k4/x+n9+m0akeGQe7dtA551sHgEa2ckDsesPD51dEgk3a21ClRcq4Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T14:49:18.650310Z","bundle_sha256":"6f5e51acb056d527584f23a31bc25e08414a1ce71c63e8b3fe49a05f8c572625"}}