{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IJ7L2WPOZ3BVLMO2CVNPDCD3OS","short_pith_number":"pith:IJ7L2WPO","schema_version":"1.0","canonical_sha256":"427ebd59eecec355b1da155af1887b748f67849b2126075dc3161850dbb4584f","source":{"kind":"arxiv","id":"1809.02735","version":1},"attestation_state":"computed","paper":{"title":"Operations Guided Neural Networks for High Fidelity Data-To-Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chin-Yew Lin, Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong Pan","submitted_at":"2018-09-08T01:49:03Z","abstract_excerpt":"Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-seque"},"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":"1809.02735","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-08T01:49:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"724b4d5c8e920b614ced5e6cf24aea1c448998084cbe093281e4a982cae8312f","abstract_canon_sha256":"0cf225ac7ecd731a8c185052606c009df5ae3d343ac47f679842d72e20a3ca2c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:15.467464Z","signature_b64":"HrYNC+BVZsEkiDNzWK/QQbXdiv9w/lnSb10nfu7fhzEee7txFNuF+FLot7F1xoIODxL3mcZ3AT+W1VxB66B7BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427ebd59eecec355b1da155af1887b748f67849b2126075dc3161850dbb4584f","last_reissued_at":"2026-05-18T00:06:15.466834Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:15.466834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Operations Guided Neural Networks for High Fidelity Data-To-Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chin-Yew Lin, Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong Pan","submitted_at":"2018-09-08T01:49:03Z","abstract_excerpt":"Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-seque"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02735","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":"1809.02735","created_at":"2026-05-18T00:06:15.466940+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.02735v1","created_at":"2026-05-18T00:06:15.466940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02735","created_at":"2026-05-18T00:06:15.466940+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ7L2WPOZ3BV","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ7L2WPOZ3BVLMO2","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ7L2WPO","created_at":"2026-05-18T12:32:31.084164+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/IJ7L2WPOZ3BVLMO2CVNPDCD3OS","json":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS.json","graph_json":"https://pith.science/api/pith-number/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/graph.json","events_json":"https://pith.science/api/pith-number/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/events.json","paper":"https://pith.science/paper/IJ7L2WPO"},"agent_actions":{"view_html":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS","download_json":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS.json","view_paper":"https://pith.science/paper/IJ7L2WPO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.02735&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/action/storage_attestation","attest_author":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/action/author_attestation","sign_citation":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/action/citation_signature","submit_replication":"https://pith.science/pith/IJ7L2WPOZ3BVLMO2CVNPDCD3OS/action/replication_record"}},"created_at":"2026-05-18T00:06:15.466940+00:00","updated_at":"2026-05-18T00:06:15.466940+00:00"}