{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:KJ43P6JF6SHYRTLFCEAO4JFWVO","short_pith_number":"pith:KJ43P6JF","schema_version":"1.0","canonical_sha256":"5279b7f925f48f88cd651100ee24b6abba66eec65f15478aa3dca45bbc6811ed","source":{"kind":"arxiv","id":"2109.03659","version":1},"attestation_state":"computed","paper":{"title":"Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ander Barrena, Eneko Agirre, Gorka Labaka, Oier Lopez de Lacalle, Oscar Sainz","submitted_at":"2021-09-08T14:04:50Z","abstract_excerpt":"Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the s"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2109.03659","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-08T14:04:50Z","cross_cats_sorted":[],"title_canon_sha256":"691905fdf05bcacba9b969b5c75ca130df9c1bdcdbe41cae6e2c905a835d8344","abstract_canon_sha256":"74ac4813046ac1e50974f73d1abf8a24198e64ca21f16110cf45e1a9e21d51c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:41.824511Z","signature_b64":"5i9WOTd7spD9uKrKRqDLLo/L/fHT8JbsUXUHF0rKyQjj4iY1KXLUHWEhqlW4SCoA4lYYoCqlhzQtRg5KCyjjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5279b7f925f48f88cd651100ee24b6abba66eec65f15478aa3dca45bbc6811ed","last_reissued_at":"2026-07-05T03:12:41.824111Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:41.824111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ander Barrena, Eneko Agirre, Gorka Labaka, Oier Lopez de Lacalle, Oscar Sainz","submitted_at":"2021-09-08T14:04:50Z","abstract_excerpt":"Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.03659","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/2109.03659/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2109.03659","created_at":"2026-07-05T03:12:41.824166+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.03659v1","created_at":"2026-07-05T03:12:41.824166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.03659","created_at":"2026-07-05T03:12:41.824166+00:00"},{"alias_kind":"pith_short_12","alias_value":"KJ43P6JF6SHY","created_at":"2026-07-05T03:12:41.824166+00:00"},{"alias_kind":"pith_short_16","alias_value":"KJ43P6JF6SHYRTLF","created_at":"2026-07-05T03:12:41.824166+00:00"},{"alias_kind":"pith_short_8","alias_value":"KJ43P6JF","created_at":"2026-07-05T03:12:41.824166+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.00649","citing_title":"GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO","json":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO.json","graph_json":"https://pith.science/api/pith-number/KJ43P6JF6SHYRTLFCEAO4JFWVO/graph.json","events_json":"https://pith.science/api/pith-number/KJ43P6JF6SHYRTLFCEAO4JFWVO/events.json","paper":"https://pith.science/paper/KJ43P6JF"},"agent_actions":{"view_html":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO","download_json":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO.json","view_paper":"https://pith.science/paper/KJ43P6JF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.03659&json=true","fetch_graph":"https://pith.science/api/pith-number/KJ43P6JF6SHYRTLFCEAO4JFWVO/graph.json","fetch_events":"https://pith.science/api/pith-number/KJ43P6JF6SHYRTLFCEAO4JFWVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO/action/storage_attestation","attest_author":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO/action/author_attestation","sign_citation":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO/action/citation_signature","submit_replication":"https://pith.science/pith/KJ43P6JF6SHYRTLFCEAO4JFWVO/action/replication_record"}},"created_at":"2026-07-05T03:12:41.824166+00:00","updated_at":"2026-07-05T03:12:41.824166+00:00"}