{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YE77F4GQRY7UITH6ZJYYHDZKVL","short_pith_number":"pith:YE77F4GQ","schema_version":"1.0","canonical_sha256":"c13ff2f0d08e3f444cfeca71838f2aaae7f360e5f8e24f6ddc8289f22c26d287","source":{"kind":"arxiv","id":"2305.00076","version":1},"attestation_state":"computed","paper":{"title":"HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aliyu Yusuf, Falalu Ibrahim Lawan, Ibrahim Said Ahmad, Idris Abdulmumin, Saheed Abdullahi Salahudeen, Saminu Mohammad Aliyu, Shamsuddeen Hassan Muhammad","submitted_at":"2023-04-28T20:03:46Z","abstract_excerpt":"We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi-level classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our m"},"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":"2305.00076","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-28T20:03:46Z","cross_cats_sorted":[],"title_canon_sha256":"4f9af21f2b07cdc85d9e77553479625eab9e264a4dcfcac78270f1a4539fca8d","abstract_canon_sha256":"33acbe227ada2f7a0c82cdac4e102aede266ffc7beee93bfb09ac172c26d8adf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:05:37.136546Z","signature_b64":"ovAYyUbdvfMre1gWnD3UZbU+qzDneC9JArCeLJ2L9ZvyIs3AJP1LvqMZZjn09hsrRD96eUpTQEDWvHAuaVCxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c13ff2f0d08e3f444cfeca71838f2aaae7f360e5f8e24f6ddc8289f22c26d287","last_reissued_at":"2026-07-05T06:05:37.135971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:05:37.135971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aliyu Yusuf, Falalu Ibrahim Lawan, Ibrahim Said Ahmad, Idris Abdulmumin, Saheed Abdullahi Salahudeen, Saminu Mohammad Aliyu, Shamsuddeen Hassan Muhammad","submitted_at":"2023-04-28T20:03:46Z","abstract_excerpt":"We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi-level classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.00076","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/2305.00076/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":"2305.00076","created_at":"2026-07-05T06:05:37.136067+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.00076v1","created_at":"2026-07-05T06:05:37.136067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.00076","created_at":"2026-07-05T06:05:37.136067+00:00"},{"alias_kind":"pith_short_12","alias_value":"YE77F4GQRY7U","created_at":"2026-07-05T06:05:37.136067+00:00"},{"alias_kind":"pith_short_16","alias_value":"YE77F4GQRY7UITH6","created_at":"2026-07-05T06:05:37.136067+00:00"},{"alias_kind":"pith_short_8","alias_value":"YE77F4GQ","created_at":"2026-07-05T06:05:37.136067+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/YE77F4GQRY7UITH6ZJYYHDZKVL","json":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL.json","graph_json":"https://pith.science/api/pith-number/YE77F4GQRY7UITH6ZJYYHDZKVL/graph.json","events_json":"https://pith.science/api/pith-number/YE77F4GQRY7UITH6ZJYYHDZKVL/events.json","paper":"https://pith.science/paper/YE77F4GQ"},"agent_actions":{"view_html":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL","download_json":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL.json","view_paper":"https://pith.science/paper/YE77F4GQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.00076&json=true","fetch_graph":"https://pith.science/api/pith-number/YE77F4GQRY7UITH6ZJYYHDZKVL/graph.json","fetch_events":"https://pith.science/api/pith-number/YE77F4GQRY7UITH6ZJYYHDZKVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL/action/storage_attestation","attest_author":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL/action/author_attestation","sign_citation":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL/action/citation_signature","submit_replication":"https://pith.science/pith/YE77F4GQRY7UITH6ZJYYHDZKVL/action/replication_record"}},"created_at":"2026-07-05T06:05:37.136067+00:00","updated_at":"2026-07-05T06:05:37.136067+00:00"}