{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:EWSIAMF32JPWKPYPP2ULBH3X2Y","short_pith_number":"pith:EWSIAMF3","schema_version":"1.0","canonical_sha256":"25a48030bbd25f653f0f7ea8b09f77d60b447fb034c8bda22921fe01eb3c7fc2","source":{"kind":"arxiv","id":"1704.08387","version":3},"attestation_state":"computed","paper":{"title":"Learning Structured Natural Language Representations for Semantic Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jianpeng Cheng, Mirella Lapata, Siva Reddy, Vijay Saraswat","submitted_at":"2017-04-27T00:24:20Z","abstract_excerpt":"We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones."},"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":"1704.08387","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-27T00:24:20Z","cross_cats_sorted":[],"title_canon_sha256":"688dae200b5b3fc33365806c0436bed3db8c736c80ebe0f7d7b08e3e58f45e99","abstract_canon_sha256":"f5812f031e844d4a8429d40d3c22c892de597c5a6bc87ea590c22d73f0dd2654"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:23.393824Z","signature_b64":"jhCgH6SKqQAOpcGmjStF1xj4tn1BAC+aepRMj/zbOYzZbP/EJ8vgbmkn6eoD5/hRspuRlC7ODffrI6A3xplNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25a48030bbd25f653f0f7ea8b09f77d60b447fb034c8bda22921fe01eb3c7fc2","last_reissued_at":"2026-05-18T00:42:23.393420Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:23.393420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Structured Natural Language Representations for Semantic Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jianpeng Cheng, Mirella Lapata, Siva Reddy, Vijay Saraswat","submitted_at":"2017-04-27T00:24:20Z","abstract_excerpt":"We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.08387","kind":"arxiv","version":3},"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":"1704.08387","created_at":"2026-05-18T00:42:23.393479+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.08387v3","created_at":"2026-05-18T00:42:23.393479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.08387","created_at":"2026-05-18T00:42:23.393479+00:00"},{"alias_kind":"pith_short_12","alias_value":"EWSIAMF32JPW","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"EWSIAMF32JPWKPYP","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"EWSIAMF3","created_at":"2026-05-18T12:31:12.930513+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/EWSIAMF32JPWKPYPP2ULBH3X2Y","json":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y.json","graph_json":"https://pith.science/api/pith-number/EWSIAMF32JPWKPYPP2ULBH3X2Y/graph.json","events_json":"https://pith.science/api/pith-number/EWSIAMF32JPWKPYPP2ULBH3X2Y/events.json","paper":"https://pith.science/paper/EWSIAMF3"},"agent_actions":{"view_html":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y","download_json":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y.json","view_paper":"https://pith.science/paper/EWSIAMF3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.08387&json=true","fetch_graph":"https://pith.science/api/pith-number/EWSIAMF32JPWKPYPP2ULBH3X2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/EWSIAMF32JPWKPYPP2ULBH3X2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y/action/storage_attestation","attest_author":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y/action/author_attestation","sign_citation":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y/action/citation_signature","submit_replication":"https://pith.science/pith/EWSIAMF32JPWKPYPP2ULBH3X2Y/action/replication_record"}},"created_at":"2026-05-18T00:42:23.393479+00:00","updated_at":"2026-05-18T00:42:23.393479+00:00"}