{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HYRZNARQFHS7IZ427BQCGW5425","short_pith_number":"pith:HYRZNARQ","schema_version":"1.0","canonical_sha256":"3e2396823029e5f4679af860235bbcd7712516c36ed89fdd31ad66c0303e976f","source":{"kind":"arxiv","id":"2209.02495","version":2},"attestation_state":"computed","paper":{"title":"Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ant\\'onio Branco, Jo\\~ao Rodrigues, Ruben Branco","submitted_at":"2022-09-06T13:38:47Z","abstract_excerpt":"Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on different pre-training objectives. It remains an issue of how much the existing pre-trained language models encompass the complexity of argument mining tasks. We rely on experimentation to shed light on how language models obtained from different lexical semantic families leverage th"},"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":"2209.02495","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-09-06T13:38:47Z","cross_cats_sorted":[],"title_canon_sha256":"3fad4b10a8ab54cd6c73aa2aecab1eb08da2b24666c533efe08123f35d4ab11d","abstract_canon_sha256":"b4fd5fecfb271c37c46c07b0adacb90d0def49193f86153b3511f3093024bb5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:21.787516Z","signature_b64":"tU5nMaWXJAgetMF8KUoYfldVH+UQMX9HVQKNfSBqGyNaon14jkODY8mFek8WY1ekmTsAOSVAgMYjUlBJ+fy9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e2396823029e5f4679af860235bbcd7712516c36ed89fdd31ad66c0303e976f","last_reissued_at":"2026-07-05T05:09:21.787108Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:21.787108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ant\\'onio Branco, Jo\\~ao Rodrigues, Ruben Branco","submitted_at":"2022-09-06T13:38:47Z","abstract_excerpt":"Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on different pre-training objectives. It remains an issue of how much the existing pre-trained language models encompass the complexity of argument mining tasks. We rely on experimentation to shed light on how language models obtained from different lexical semantic families leverage th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.02495","kind":"arxiv","version":2},"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/2209.02495/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":"2209.02495","created_at":"2026-07-05T05:09:21.787170+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.02495v2","created_at":"2026-07-05T05:09:21.787170+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.02495","created_at":"2026-07-05T05:09:21.787170+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYRZNARQFHS7","created_at":"2026-07-05T05:09:21.787170+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYRZNARQFHS7IZ42","created_at":"2026-07-05T05:09:21.787170+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYRZNARQ","created_at":"2026-07-05T05:09:21.787170+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425","json":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425.json","graph_json":"https://pith.science/api/pith-number/HYRZNARQFHS7IZ427BQCGW5425/graph.json","events_json":"https://pith.science/api/pith-number/HYRZNARQFHS7IZ427BQCGW5425/events.json","paper":"https://pith.science/paper/HYRZNARQ"},"agent_actions":{"view_html":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425","download_json":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425.json","view_paper":"https://pith.science/paper/HYRZNARQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.02495&json=true","fetch_graph":"https://pith.science/api/pith-number/HYRZNARQFHS7IZ427BQCGW5425/graph.json","fetch_events":"https://pith.science/api/pith-number/HYRZNARQFHS7IZ427BQCGW5425/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425/action/storage_attestation","attest_author":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425/action/author_attestation","sign_citation":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425/action/citation_signature","submit_replication":"https://pith.science/pith/HYRZNARQFHS7IZ427BQCGW5425/action/replication_record"}},"created_at":"2026-07-05T05:09:21.787170+00:00","updated_at":"2026-07-05T05:09:21.787170+00:00"}