{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PBRGYB5KZHDTCKOHPCXZC427UP","short_pith_number":"pith:PBRGYB5K","schema_version":"1.0","canonical_sha256":"78626c07aac9c73129c778af91735fa3db6f56ba4e915bcddcc3245ab34655bc","source":{"kind":"arxiv","id":"2504.17252","version":1},"attestation_state":"computed","paper":{"title":"Low-Resource Neural Machine Translation Using Recurrent Neural Networks and Transfer Learning: A Case Study on English-to-Igbo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Biswarup Das, Ocheme Anthony Ekle","submitted_at":"2025-04-24T05:02:26Z","abstract_excerpt":"In this study, we develop Neural Machine Translation (NMT) and Transformer-based transfer learning models for English-to-Igbo translation - a low-resource African language spoken by over 40 million people across Nigeria and West Africa. Our models are trained on a curated and benchmarked dataset compiled from Bible corpora, local news, Wikipedia articles, and Common Crawl, all verified by native language experts. We leverage Recurrent Neural Network (RNN) architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), enhanced with attention mechanisms to improve transl"},"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":"2504.17252","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T05:02:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4e3ab9c88a306345b64d75674bafa77c87f263e04543103816f0f01873c526bd","abstract_canon_sha256":"859f822744683f72e93fda98fe6fbe5fed5aaefa324ad9a1aab1b4bf169a77b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:25.244748Z","signature_b64":"wqWxALX1/iru45wdnt89BPI8UZe7C0uzxySL7TQeZSM9QTEZLdipSPUdygsExdf6We0lWyqwLCGRm4fdjVwnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78626c07aac9c73129c778af91735fa3db6f56ba4e915bcddcc3245ab34655bc","last_reissued_at":"2026-07-05T10:53:25.244228Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:25.244228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Low-Resource Neural Machine Translation Using Recurrent Neural Networks and Transfer Learning: A Case Study on English-to-Igbo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Biswarup Das, Ocheme Anthony Ekle","submitted_at":"2025-04-24T05:02:26Z","abstract_excerpt":"In this study, we develop Neural Machine Translation (NMT) and Transformer-based transfer learning models for English-to-Igbo translation - a low-resource African language spoken by over 40 million people across Nigeria and West Africa. Our models are trained on a curated and benchmarked dataset compiled from Bible corpora, local news, Wikipedia articles, and Common Crawl, all verified by native language experts. We leverage Recurrent Neural Network (RNN) architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), enhanced with attention mechanisms to improve transl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17252","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/2504.17252/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":"2504.17252","created_at":"2026-07-05T10:53:25.244296+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17252v1","created_at":"2026-07-05T10:53:25.244296+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17252","created_at":"2026-07-05T10:53:25.244296+00:00"},{"alias_kind":"pith_short_12","alias_value":"PBRGYB5KZHDT","created_at":"2026-07-05T10:53:25.244296+00:00"},{"alias_kind":"pith_short_16","alias_value":"PBRGYB5KZHDTCKOH","created_at":"2026-07-05T10:53:25.244296+00:00"},{"alias_kind":"pith_short_8","alias_value":"PBRGYB5K","created_at":"2026-07-05T10:53:25.244296+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/PBRGYB5KZHDTCKOHPCXZC427UP","json":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP.json","graph_json":"https://pith.science/api/pith-number/PBRGYB5KZHDTCKOHPCXZC427UP/graph.json","events_json":"https://pith.science/api/pith-number/PBRGYB5KZHDTCKOHPCXZC427UP/events.json","paper":"https://pith.science/paper/PBRGYB5K"},"agent_actions":{"view_html":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP","download_json":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP.json","view_paper":"https://pith.science/paper/PBRGYB5K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17252&json=true","fetch_graph":"https://pith.science/api/pith-number/PBRGYB5KZHDTCKOHPCXZC427UP/graph.json","fetch_events":"https://pith.science/api/pith-number/PBRGYB5KZHDTCKOHPCXZC427UP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP/action/storage_attestation","attest_author":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP/action/author_attestation","sign_citation":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP/action/citation_signature","submit_replication":"https://pith.science/pith/PBRGYB5KZHDTCKOHPCXZC427UP/action/replication_record"}},"created_at":"2026-07-05T10:53:25.244296+00:00","updated_at":"2026-07-05T10:53:25.244296+00:00"}