{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WL5ACJSV4GABDXL26JRDGTROQ5","short_pith_number":"pith:WL5ACJSV","schema_version":"1.0","canonical_sha256":"b2fa012655e18011dd7af262334e2e8754535df1257bda594bcade518bb0d577","source":{"kind":"arxiv","id":"1810.12118","version":1},"attestation_state":"computed","paper":{"title":"Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Helen Jiahe Zhao, Jiamou Liu","submitted_at":"2018-10-26T12:34:21Z","abstract_excerpt":"Question answering (QA) has significantly benefitted from deep learning techniques in recent years. However, domain-specific QA remains a challenge due to the significant amount of data required to train a neural network. This paper studies the answer sentence selection task in the Bible domain and answer questions by selecting relevant verses from the Bible. For this purpose, we create a new dataset BibleQA based on bible trivia questions and propose three neural network models for our task. We pre-train our models on a large-scale QA dataset, SQuAD, and investigate the effect of transferring"},"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":"1810.12118","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-10-26T12:34:21Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"19049d97e9229dc4c78f4fcf99877014154009b60018b5f4b67528e7194727cb","abstract_canon_sha256":"6775dee4e61d1c78ca123e969ff751da2ab20e0060198feaed3e48ab75476e98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:04.252748Z","signature_b64":"ugS2bE8S5HPP4pirvXIGQm8yuHa7pLW3RI1v8hr52p1VYbnieVwsxRNYiaz3+8ZOUxv5K0VCsQdN13GeUNEDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2fa012655e18011dd7af262334e2e8754535df1257bda594bcade518bb0d577","last_reissued_at":"2026-05-18T00:02:04.252091Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:04.252091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Helen Jiahe Zhao, Jiamou Liu","submitted_at":"2018-10-26T12:34:21Z","abstract_excerpt":"Question answering (QA) has significantly benefitted from deep learning techniques in recent years. However, domain-specific QA remains a challenge due to the significant amount of data required to train a neural network. This paper studies the answer sentence selection task in the Bible domain and answer questions by selecting relevant verses from the Bible. For this purpose, we create a new dataset BibleQA based on bible trivia questions and propose three neural network models for our task. We pre-train our models on a large-scale QA dataset, SQuAD, and investigate the effect of transferring"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12118","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":""},"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":"1810.12118","created_at":"2026-05-18T00:02:04.252185+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.12118v1","created_at":"2026-05-18T00:02:04.252185+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12118","created_at":"2026-05-18T00:02:04.252185+00:00"},{"alias_kind":"pith_short_12","alias_value":"WL5ACJSV4GAB","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WL5ACJSV4GABDXL2","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WL5ACJSV","created_at":"2026-05-18T12:33:01.666342+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/WL5ACJSV4GABDXL26JRDGTROQ5","json":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5.json","graph_json":"https://pith.science/api/pith-number/WL5ACJSV4GABDXL26JRDGTROQ5/graph.json","events_json":"https://pith.science/api/pith-number/WL5ACJSV4GABDXL26JRDGTROQ5/events.json","paper":"https://pith.science/paper/WL5ACJSV"},"agent_actions":{"view_html":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5","download_json":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5.json","view_paper":"https://pith.science/paper/WL5ACJSV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.12118&json=true","fetch_graph":"https://pith.science/api/pith-number/WL5ACJSV4GABDXL26JRDGTROQ5/graph.json","fetch_events":"https://pith.science/api/pith-number/WL5ACJSV4GABDXL26JRDGTROQ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5/action/storage_attestation","attest_author":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5/action/author_attestation","sign_citation":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5/action/citation_signature","submit_replication":"https://pith.science/pith/WL5ACJSV4GABDXL26JRDGTROQ5/action/replication_record"}},"created_at":"2026-05-18T00:02:04.252185+00:00","updated_at":"2026-05-18T00:02:04.252185+00:00"}