{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:CEAXX3YJWZWYARSHTO3DTHJTST","short_pith_number":"pith:CEAXX3YJ","schema_version":"1.0","canonical_sha256":"11017bef09b66d8046479bb6399d3394cc59129b6405f525b62603322aa1ed5d","source":{"kind":"arxiv","id":"2010.04806","version":2},"attestation_state":"computed","paper":{"title":"AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Giovanni Campagna, Monica S. Lam, Silei Xu, Sina J. Semnani","submitted_at":"2020-10-09T21:06:57Z","abstract_excerpt":"We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraphrasing combined with template-based parsing to find alternative expressions of an attribute in different parts of speech. It also uses a novel filtered auto-paraphraser to generate correct paraphrases of entire sentences. We apply AutoQA to the Schema2QA dataset and obtain an ave"},"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":"2010.04806","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-09T21:06:57Z","cross_cats_sorted":[],"title_canon_sha256":"f08a4b7d376c349e95e3c57a81364d7ac65ab99cf053eac50b41ea69ed41ff2b","abstract_canon_sha256":"8146db44a81c38022ef6a9873447a2e0a869a3d8ba6e37e9d720434c34dc04d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:01.677425Z","signature_b64":"ky9LbugclIwysJf6FO4rd6YvKgVf8u+4fG2/DNAGucvu8sOG9rWj+ctUdfAe9tlkRJg/hzl1dnbeakTj1ZQdCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11017bef09b66d8046479bb6399d3394cc59129b6405f525b62603322aa1ed5d","last_reissued_at":"2026-07-05T02:47:01.676970Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:01.676970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Giovanni Campagna, Monica S. Lam, Silei Xu, Sina J. Semnani","submitted_at":"2020-10-09T21:06:57Z","abstract_excerpt":"We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraphrasing combined with template-based parsing to find alternative expressions of an attribute in different parts of speech. It also uses a novel filtered auto-paraphraser to generate correct paraphrases of entire sentences. We apply AutoQA to the Schema2QA dataset and obtain an ave"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.04806","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/2010.04806/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":"2010.04806","created_at":"2026-07-05T02:47:01.677025+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.04806v2","created_at":"2026-07-05T02:47:01.677025+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.04806","created_at":"2026-07-05T02:47:01.677025+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEAXX3YJWZWY","created_at":"2026-07-05T02:47:01.677025+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEAXX3YJWZWYARSH","created_at":"2026-07-05T02:47:01.677025+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEAXX3YJ","created_at":"2026-07-05T02:47:01.677025+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/CEAXX3YJWZWYARSHTO3DTHJTST","json":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST.json","graph_json":"https://pith.science/api/pith-number/CEAXX3YJWZWYARSHTO3DTHJTST/graph.json","events_json":"https://pith.science/api/pith-number/CEAXX3YJWZWYARSHTO3DTHJTST/events.json","paper":"https://pith.science/paper/CEAXX3YJ"},"agent_actions":{"view_html":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST","download_json":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST.json","view_paper":"https://pith.science/paper/CEAXX3YJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.04806&json=true","fetch_graph":"https://pith.science/api/pith-number/CEAXX3YJWZWYARSHTO3DTHJTST/graph.json","fetch_events":"https://pith.science/api/pith-number/CEAXX3YJWZWYARSHTO3DTHJTST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST/action/storage_attestation","attest_author":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST/action/author_attestation","sign_citation":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST/action/citation_signature","submit_replication":"https://pith.science/pith/CEAXX3YJWZWYARSHTO3DTHJTST/action/replication_record"}},"created_at":"2026-07-05T02:47:01.677025+00:00","updated_at":"2026-07-05T02:47:01.677025+00:00"}