{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:M6BOEA43VVG2JL6UEVIPNSDBXC","short_pith_number":"pith:M6BOEA43","schema_version":"1.0","canonical_sha256":"6782e2039bad4da4afd42550f6c861b88fee3fd0d14487a0c977ca48b3fc9aac","source":{"kind":"arxiv","id":"1601.01280","version":2},"attestation_state":"computed","paper":{"title":"Language to Logical Form with Neural Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Li Dong, Mirella Lapata","submitted_at":"2016-01-06T19:13:12Z","abstract_excerpt":"Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively wit"},"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":"1601.01280","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-01-06T19:13:12Z","cross_cats_sorted":[],"title_canon_sha256":"b4838d22ae6a7fc561a83315674205323b53ad3e7185c4bbe83ec9012f23efde","abstract_canon_sha256":"18b57c9c584bada551c7ce5f827841a5ccd3389ce016105760401a0157a809c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:49.226894Z","signature_b64":"bOQHIHtemJhueiQddEIn5ujJxCY1DTzv3BSonaNJ6jRXtUqnWwSvqRQBd84IBXI3hrJ4RCb1cqtzZ6oe9U68Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6782e2039bad4da4afd42550f6c861b88fee3fd0d14487a0c977ca48b3fc9aac","last_reissued_at":"2026-05-18T01:12:49.226562Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:49.226562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language to Logical Form with Neural Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Li Dong, Mirella Lapata","submitted_at":"2016-01-06T19:13:12Z","abstract_excerpt":"Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.01280","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":""},"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":"1601.01280","created_at":"2026-05-18T01:12:49.226616+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.01280v2","created_at":"2026-05-18T01:12:49.226616+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.01280","created_at":"2026-05-18T01:12:49.226616+00:00"},{"alias_kind":"pith_short_12","alias_value":"M6BOEA43VVG2","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"M6BOEA43VVG2JL6U","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"M6BOEA43","created_at":"2026-05-18T12:30:29.479603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1907.08584","citing_title":"CraftAssist: A Framework for Dialogue-enabled Interactive Agents","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"1907.09273","citing_title":"Why Build an Assistant in Minecraft?","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC","json":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC.json","graph_json":"https://pith.science/api/pith-number/M6BOEA43VVG2JL6UEVIPNSDBXC/graph.json","events_json":"https://pith.science/api/pith-number/M6BOEA43VVG2JL6UEVIPNSDBXC/events.json","paper":"https://pith.science/paper/M6BOEA43"},"agent_actions":{"view_html":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC","download_json":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC.json","view_paper":"https://pith.science/paper/M6BOEA43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.01280&json=true","fetch_graph":"https://pith.science/api/pith-number/M6BOEA43VVG2JL6UEVIPNSDBXC/graph.json","fetch_events":"https://pith.science/api/pith-number/M6BOEA43VVG2JL6UEVIPNSDBXC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC/action/storage_attestation","attest_author":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC/action/author_attestation","sign_citation":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC/action/citation_signature","submit_replication":"https://pith.science/pith/M6BOEA43VVG2JL6UEVIPNSDBXC/action/replication_record"}},"created_at":"2026-05-18T01:12:49.226616+00:00","updated_at":"2026-05-18T01:12:49.226616+00:00"}