{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OYSBL5TG66EIG6GHB6XAMMU5F3","short_pith_number":"pith:OYSBL5TG","schema_version":"1.0","canonical_sha256":"762415f666f7888378c70fae06329d2ede3ffb616e95bc1f9816e66d45c3454e","source":{"kind":"arxiv","id":"1911.04942","version":5},"attestation_state":"computed","paper":{"title":"RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bailin Wang, Matthew Richardson, Oleksandr Polozov, Richard Shin, XiaoDong Liu","submitted_at":"2019-11-10T09:09:13Z","abstract_excerpt":"When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging "},"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":"1911.04942","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-10T09:09:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c099fe4e510ba4a4fad9f14bb9f939c0b5b5826116140b1dd9025b9fbbf9dfe7","abstract_canon_sha256":"ec7a2c22397f6f6c79ed6ce469158ff711f9f7ec0ed7a3c784cf5e31f724ef6a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:08:05.306412Z","signature_b64":"ga2qYXSEzWQ+hZJAFKQg9snHIvpasz6mXRrt/ms33SAHSgw5uEoh8xIm7bcuc3i3XH6vyK4Ij6lic5/I1yniCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"762415f666f7888378c70fae06329d2ede3ffb616e95bc1f9816e66d45c3454e","last_reissued_at":"2026-07-05T03:08:05.305696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:08:05.305696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bailin Wang, Matthew Richardson, Oleksandr Polozov, Richard Shin, XiaoDong Liu","submitted_at":"2019-11-10T09:09:13Z","abstract_excerpt":"When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.04942","kind":"arxiv","version":5},"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/1911.04942/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":"1911.04942","created_at":"2026-07-05T03:08:05.305781+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.04942v5","created_at":"2026-07-05T03:08:05.305781+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.04942","created_at":"2026-07-05T03:08:05.305781+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYSBL5TG66EI","created_at":"2026-07-05T03:08:05.305781+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYSBL5TG66EIG6GH","created_at":"2026-07-05T03:08:05.305781+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYSBL5TG","created_at":"2026-07-05T03:08:05.305781+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04500","citing_title":"SANE Schema-aware Natural-language Evaluation of Biological Data","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2405.16755","citing_title":"CHESS: Contextual Harnessing for Efficient SQL Synthesis","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2604.28049","citing_title":"Agent-Agnostic Evaluation of SQL Accuracy in Production Text-to-SQL Systems","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10318","citing_title":"Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2105.09938","citing_title":"Measuring Coding Challenge Competence With APPS","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3","json":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3.json","graph_json":"https://pith.science/api/pith-number/OYSBL5TG66EIG6GHB6XAMMU5F3/graph.json","events_json":"https://pith.science/api/pith-number/OYSBL5TG66EIG6GHB6XAMMU5F3/events.json","paper":"https://pith.science/paper/OYSBL5TG"},"agent_actions":{"view_html":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3","download_json":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3.json","view_paper":"https://pith.science/paper/OYSBL5TG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.04942&json=true","fetch_graph":"https://pith.science/api/pith-number/OYSBL5TG66EIG6GHB6XAMMU5F3/graph.json","fetch_events":"https://pith.science/api/pith-number/OYSBL5TG66EIG6GHB6XAMMU5F3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3/action/storage_attestation","attest_author":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3/action/author_attestation","sign_citation":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3/action/citation_signature","submit_replication":"https://pith.science/pith/OYSBL5TG66EIG6GHB6XAMMU5F3/action/replication_record"}},"created_at":"2026-07-05T03:08:05.305781+00:00","updated_at":"2026-07-05T03:08:05.305781+00:00"}