{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:MYZRJJAIZICOBXCQWBXXN5MPMT","short_pith_number":"pith:MYZRJJAI","canonical_record":{"source":{"id":"2004.13845","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-04-06T14:38:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ead8589d17170761c2e38bd87b0646bcfd30feebc77709987719e694b19dbbf3","abstract_canon_sha256":"d20d44daa277de413e9bd7384257965fd24e90c8b7e4d0b939858c514f105ebe"},"schema_version":"1.0"},"canonical_sha256":"663314a408ca04e0dc50b06f76f58f64c6a7db004b6453c310783e496887deb3","source":{"kind":"arxiv","id":"2004.13845","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.13845","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"arxiv_version","alias_value":"2004.13845v1","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.13845","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"pith_short_12","alias_value":"MYZRJJAIZICO","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"pith_short_16","alias_value":"MYZRJJAIZICOBXCQ","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"pith_short_8","alias_value":"MYZRJJAI","created_at":"2026-07-05T00:59:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:MYZRJJAIZICOBXCQWBXXN5MPMT","target":"record","payload":{"canonical_record":{"source":{"id":"2004.13845","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-04-06T14:38:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ead8589d17170761c2e38bd87b0646bcfd30feebc77709987719e694b19dbbf3","abstract_canon_sha256":"d20d44daa277de413e9bd7384257965fd24e90c8b7e4d0b939858c514f105ebe"},"schema_version":"1.0"},"canonical_sha256":"663314a408ca04e0dc50b06f76f58f64c6a7db004b6453c310783e496887deb3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:59:04.773492Z","signature_b64":"FG/NJTaMxf5Wk9NI8AePD9HFs0l4gvvZUgUyNOoqg7Ems6vQeilxQw/u7NOhWA4Xhn3pwvlWduZRcMBoSDypBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"663314a408ca04e0dc50b06f76f58f64c6a7db004b6453c310783e496887deb3","last_reissued_at":"2026-07-05T00:59:04.773004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:59:04.773004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2004.13845","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":"2026-07-05T00:59:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FYZjm4kOl6DNw+yJ1g/dD66upQ0cPVS/WnUUXk0vlsnDn0jN1oRJqxqlpjLzHy++R8E1jxBgnFaOkfnXsRbKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:15:51.948189Z"},"content_sha256":"9739346839a2423db47a6c43b2c304d9ed11e59392e71952dd0400a515dad2bf","schema_version":"1.0","event_id":"sha256:9739346839a2423db47a6c43b2c304d9ed11e59392e71952dd0400a515dad2bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:MYZRJJAIZICOBXCQWBXXN5MPMT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DARE: Data Augmented Relation Extraction with GPT-2","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Andrea Pierleoni, Yannis Papanikolaou","submitted_at":"2020-04-06T14:38:36Z","abstract_excerpt":"Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues. In this work, we present Data Augmented Relation Extraction(DARE), a simple method to augment training data by properly fine-tuning GPT-2 to generate examples for specific relation types. The generated training data is then used in combination with the gold dataset to train a BERT-based RE classifier. In a series of experiments we show the advantages of our method, which leads in improvements of up to 11 F1 score points against a strong base-line. Also, DARE ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.13845","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2004.13845/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":"2026-07-05T00:59:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u6R5kBgBv1lD3bH3pen+QY1YZcdZJs4nDqNKgGy86sDKTRMlr/Ifd/BLfp0Nyf89WdU4jeVYrF077c8rtnaZBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:15:51.948572Z"},"content_sha256":"0fc0b2c7ac6276eaa10fca7b789517fe38ecd38f125365dfccc2427eeea34efc","schema_version":"1.0","event_id":"sha256:0fc0b2c7ac6276eaa10fca7b789517fe38ecd38f125365dfccc2427eeea34efc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MYZRJJAIZICOBXCQWBXXN5MPMT/bundle.json","state_url":"https://pith.science/pith/MYZRJJAIZICOBXCQWBXXN5MPMT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MYZRJJAIZICOBXCQWBXXN5MPMT/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":"2026-08-18T23:15:51Z","links":{"resolver":"https://pith.science/pith/MYZRJJAIZICOBXCQWBXXN5MPMT","bundle":"https://pith.science/pith/MYZRJJAIZICOBXCQWBXXN5MPMT/bundle.json","state":"https://pith.science/pith/MYZRJJAIZICOBXCQWBXXN5MPMT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MYZRJJAIZICOBXCQWBXXN5MPMT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:MYZRJJAIZICOBXCQWBXXN5MPMT","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":"d20d44daa277de413e9bd7384257965fd24e90c8b7e4d0b939858c514f105ebe","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-04-06T14:38:36Z","title_canon_sha256":"ead8589d17170761c2e38bd87b0646bcfd30feebc77709987719e694b19dbbf3"},"schema_version":"1.0","source":{"id":"2004.13845","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.13845","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"arxiv_version","alias_value":"2004.13845v1","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.13845","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"pith_short_12","alias_value":"MYZRJJAIZICO","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"pith_short_16","alias_value":"MYZRJJAIZICOBXCQ","created_at":"2026-07-05T00:59:04Z"},{"alias_kind":"pith_short_8","alias_value":"MYZRJJAI","created_at":"2026-07-05T00:59:04Z"}],"graph_snapshots":[{"event_id":"sha256:0fc0b2c7ac6276eaa10fca7b789517fe38ecd38f125365dfccc2427eeea34efc","target":"graph","created_at":"2026-07-05T00:59:04Z","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/2004.13845/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues. In this work, we present Data Augmented Relation Extraction(DARE), a simple method to augment training data by properly fine-tuning GPT-2 to generate examples for specific relation types. The generated training data is then used in combination with the gold dataset to train a BERT-based RE classifier. In a series of experiments we show the advantages of our method, which leads in improvements of up to 11 F1 score points against a strong base-line. Also, DARE ac","authors_text":"Andrea Pierleoni, Yannis Papanikolaou","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-04-06T14:38:36Z","title":"DARE: Data Augmented Relation Extraction with GPT-2"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.13845","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":"sha256:9739346839a2423db47a6c43b2c304d9ed11e59392e71952dd0400a515dad2bf","target":"record","created_at":"2026-07-05T00:59:04Z","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":"d20d44daa277de413e9bd7384257965fd24e90c8b7e4d0b939858c514f105ebe","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-04-06T14:38:36Z","title_canon_sha256":"ead8589d17170761c2e38bd87b0646bcfd30feebc77709987719e694b19dbbf3"},"schema_version":"1.0","source":{"id":"2004.13845","kind":"arxiv","version":1}},"canonical_sha256":"663314a408ca04e0dc50b06f76f58f64c6a7db004b6453c310783e496887deb3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"663314a408ca04e0dc50b06f76f58f64c6a7db004b6453c310783e496887deb3","first_computed_at":"2026-07-05T00:59:04.773004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:59:04.773004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FG/NJTaMxf5Wk9NI8AePD9HFs0l4gvvZUgUyNOoqg7Ems6vQeilxQw/u7NOhWA4Xhn3pwvlWduZRcMBoSDypBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:59:04.773492Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.13845","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9739346839a2423db47a6c43b2c304d9ed11e59392e71952dd0400a515dad2bf","sha256:0fc0b2c7ac6276eaa10fca7b789517fe38ecd38f125365dfccc2427eeea34efc"],"state_sha256":"1c3c991d2c3591a463f9c19ec571e86ddcaff5bd016e015cf59dc0b219d5ff1e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BZtNGjBmy6tc46eN1r6jXTOFC5yZLwTzsLaalObzjGr99I8J6zhaDEVUrTBlgp+1b7BmlWXWb+RQcIebKqaOAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T23:15:51.950921Z","bundle_sha256":"4259db2e3fa5184b7ea22bcffa947ff33cbfae04f7e8d594e93d63134aeadd1f"}}