{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KXBYQOG2ES272HH2U6PGS2NZUP","short_pith_number":"pith:KXBYQOG2","schema_version":"1.0","canonical_sha256":"55c38838da24b5fd1cfaa79e6969b9a3cb129deeb04a509dedd02930ba710e8a","source":{"kind":"arxiv","id":"1905.11531","version":1},"attestation_state":"computed","paper":{"title":"Compositional pre-training for neural semantic parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Amir Ziai","submitted_at":"2019-05-27T22:51:39Z","abstract_excerpt":"Semantic parsing is the process of translating natural language utterances into logical forms, which has many important applications such as question answering and instruction following. Sequence-to-sequence models have been very successful across many NLP tasks. However, a lack of task-specific prior knowledge can be detrimental to the performance of these models. Prior work has used frameworks for inducing grammars over the training examples, which capture conditional independence properties that the model can leverage. Inspired by the recent success stories such as BERT we set out to extend"},"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":"1905.11531","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-27T22:51:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"db84cdade723a124fdc776d95b985274f9ac0574c45dbf5a3154c387c5094bcd","abstract_canon_sha256":"974c7da9a579274a8834e4a60b713b4f2b34aaed8b517d4afaa70264bfa18b57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:53.126559Z","signature_b64":"9iQqC/vw6RmS2qVDhEH23iRirhk8utbqR4njEs2NKUqR0ZBAWDTG/sOZh5yM/bIF6zacAnyHHnAAr046S7B7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55c38838da24b5fd1cfaa79e6969b9a3cb129deeb04a509dedd02930ba710e8a","last_reissued_at":"2026-05-17T23:44:53.126028Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:53.126028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compositional pre-training for neural semantic parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Amir Ziai","submitted_at":"2019-05-27T22:51:39Z","abstract_excerpt":"Semantic parsing is the process of translating natural language utterances into logical forms, which has many important applications such as question answering and instruction following. Sequence-to-sequence models have been very successful across many NLP tasks. However, a lack of task-specific prior knowledge can be detrimental to the performance of these models. Prior work has used frameworks for inducing grammars over the training examples, which capture conditional independence properties that the model can leverage. Inspired by the recent success stories such as BERT we set out to extend"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11531","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":"1905.11531","created_at":"2026-05-17T23:44:53.126116+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.11531v1","created_at":"2026-05-17T23:44:53.126116+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11531","created_at":"2026-05-17T23:44:53.126116+00:00"},{"alias_kind":"pith_short_12","alias_value":"KXBYQOG2ES27","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KXBYQOG2ES272HH2","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KXBYQOG2","created_at":"2026-05-18T12:33:21.387695+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/KXBYQOG2ES272HH2U6PGS2NZUP","json":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP.json","graph_json":"https://pith.science/api/pith-number/KXBYQOG2ES272HH2U6PGS2NZUP/graph.json","events_json":"https://pith.science/api/pith-number/KXBYQOG2ES272HH2U6PGS2NZUP/events.json","paper":"https://pith.science/paper/KXBYQOG2"},"agent_actions":{"view_html":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP","download_json":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP.json","view_paper":"https://pith.science/paper/KXBYQOG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.11531&json=true","fetch_graph":"https://pith.science/api/pith-number/KXBYQOG2ES272HH2U6PGS2NZUP/graph.json","fetch_events":"https://pith.science/api/pith-number/KXBYQOG2ES272HH2U6PGS2NZUP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP/action/storage_attestation","attest_author":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP/action/author_attestation","sign_citation":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP/action/citation_signature","submit_replication":"https://pith.science/pith/KXBYQOG2ES272HH2U6PGS2NZUP/action/replication_record"}},"created_at":"2026-05-17T23:44:53.126116+00:00","updated_at":"2026-05-17T23:44:53.126116+00:00"}