{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5F5VJDSCXW43MKS6ENWB67QHKM","short_pith_number":"pith:5F5VJDSC","canonical_record":{"source":{"id":"1808.02747","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-08T13:12:10Z","cross_cats_sorted":[],"title_canon_sha256":"8c05775d501a4ca7a5b3c8b7ca633c4c818491fb6a0d029a32928957a166b481","abstract_canon_sha256":"93b0d267cadf9e87231a6f9160574ec19a2655018f87aecacfe9b3f7ff6335a3"},"schema_version":"1.0"},"canonical_sha256":"e97b548e42bdb9b62a5e236c1f7e075314b9270c9d11abc5c832de9876e4e7c1","source":{"kind":"arxiv","id":"1808.02747","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.02747","created_at":"2026-05-18T00:08:30Z"},{"alias_kind":"arxiv_version","alias_value":"1808.02747v2","created_at":"2026-05-18T00:08:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02747","created_at":"2026-05-18T00:08:30Z"},{"alias_kind":"pith_short_12","alias_value":"5F5VJDSCXW43","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5F5VJDSCXW43MKS6","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5F5VJDSC","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5F5VJDSCXW43MKS6ENWB67QHKM","target":"record","payload":{"canonical_record":{"source":{"id":"1808.02747","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-08T13:12:10Z","cross_cats_sorted":[],"title_canon_sha256":"8c05775d501a4ca7a5b3c8b7ca633c4c818491fb6a0d029a32928957a166b481","abstract_canon_sha256":"93b0d267cadf9e87231a6f9160574ec19a2655018f87aecacfe9b3f7ff6335a3"},"schema_version":"1.0"},"canonical_sha256":"e97b548e42bdb9b62a5e236c1f7e075314b9270c9d11abc5c832de9876e4e7c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:30.728750Z","signature_b64":"Huquzbjpazq4cZgClQR+zSx1v69bG9dSv5GVI+nObQXDhBfbnk+Uy+dGPch4jjJ17rL9tR0AggFEJIGvAr1FBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e97b548e42bdb9b62a5e236c1f7e075314b9270c9d11abc5c832de9876e4e7c1","last_reissued_at":"2026-05-18T00:08:30.728390Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:30.728390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.02747","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:08:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3GGrW/2DNTVizS8+/QfMfIOTG5O9z+pVLaSgW/OnNge0ngG2rS1W7uytXFK3Y/Bre9wmcOGjk6OA54kYVIwCBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:21:08.454423Z"},"content_sha256":"9edf299bd5e67e0fb447be33f9ee40b6ea039004b0aaaa511202de6ed76ae5bd","schema_version":"1.0","event_id":"sha256:9edf299bd5e67e0fb447be33f9ee40b6ea039004b0aaaa511202de6ed76ae5bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5F5VJDSCXW43MKS6ENWB67QHKM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Natural Language Generation by Hierarchical Decoding with Linguistic Patterns","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Kai-Ling Lo, Shang-Yu Su, Yi-Ting Yeh, Yun-Nung Chen","submitted_at":"2018-08-08T13:12:10Z","abstract_excerpt":"Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02747","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:08:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6GJl2HkoUYDfTgifTpbmMMl9hbhy0KN4fQnz0pOUOMfZ7BOUtBE671YIKxXMt0g/e4TPimRZCbJsWmP5OnwgBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:21:08.455006Z"},"content_sha256":"6dc1660c32916e6b7ffb875a3af17bea09d457f582e59eac4f6d891a2b4ed1b7","schema_version":"1.0","event_id":"sha256:6dc1660c32916e6b7ffb875a3af17bea09d457f582e59eac4f6d891a2b4ed1b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5F5VJDSCXW43MKS6ENWB67QHKM/bundle.json","state_url":"https://pith.science/pith/5F5VJDSCXW43MKS6ENWB67QHKM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5F5VJDSCXW43MKS6ENWB67QHKM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T13:21:08Z","links":{"resolver":"https://pith.science/pith/5F5VJDSCXW43MKS6ENWB67QHKM","bundle":"https://pith.science/pith/5F5VJDSCXW43MKS6ENWB67QHKM/bundle.json","state":"https://pith.science/pith/5F5VJDSCXW43MKS6ENWB67QHKM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5F5VJDSCXW43MKS6ENWB67QHKM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5F5VJDSCXW43MKS6ENWB67QHKM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"93b0d267cadf9e87231a6f9160574ec19a2655018f87aecacfe9b3f7ff6335a3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-08T13:12:10Z","title_canon_sha256":"8c05775d501a4ca7a5b3c8b7ca633c4c818491fb6a0d029a32928957a166b481"},"schema_version":"1.0","source":{"id":"1808.02747","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.02747","created_at":"2026-05-18T00:08:30Z"},{"alias_kind":"arxiv_version","alias_value":"1808.02747v2","created_at":"2026-05-18T00:08:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02747","created_at":"2026-05-18T00:08:30Z"},{"alias_kind":"pith_short_12","alias_value":"5F5VJDSCXW43","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5F5VJDSCXW43MKS6","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5F5VJDSC","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:6dc1660c32916e6b7ffb875a3af17bea09d457f582e59eac4f6d891a2b4ed1b7","target":"graph","created_at":"2026-05-18T00:08:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple ","authors_text":"Kai-Ling Lo, Shang-Yu Su, Yi-Ting Yeh, Yun-Nung Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-08T13:12:10Z","title":"Natural Language Generation by Hierarchical Decoding with Linguistic Patterns"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02747","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9edf299bd5e67e0fb447be33f9ee40b6ea039004b0aaaa511202de6ed76ae5bd","target":"record","created_at":"2026-05-18T00:08:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"93b0d267cadf9e87231a6f9160574ec19a2655018f87aecacfe9b3f7ff6335a3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-08T13:12:10Z","title_canon_sha256":"8c05775d501a4ca7a5b3c8b7ca633c4c818491fb6a0d029a32928957a166b481"},"schema_version":"1.0","source":{"id":"1808.02747","kind":"arxiv","version":2}},"canonical_sha256":"e97b548e42bdb9b62a5e236c1f7e075314b9270c9d11abc5c832de9876e4e7c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e97b548e42bdb9b62a5e236c1f7e075314b9270c9d11abc5c832de9876e4e7c1","first_computed_at":"2026-05-18T00:08:30.728390Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:30.728390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Huquzbjpazq4cZgClQR+zSx1v69bG9dSv5GVI+nObQXDhBfbnk+Uy+dGPch4jjJ17rL9tR0AggFEJIGvAr1FBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:30.728750Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.02747","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9edf299bd5e67e0fb447be33f9ee40b6ea039004b0aaaa511202de6ed76ae5bd","sha256:6dc1660c32916e6b7ffb875a3af17bea09d457f582e59eac4f6d891a2b4ed1b7"],"state_sha256":"7d8487a789e6949ab134e72ca262a90dbe04059c2b50bd38e92e109136eaed37"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O08vjpe+yRXS9EBuTLNuSgiI70c3l/SZ3siMMG2oepA+RwPEbkU/+UA9YesN+ExzxbOeNStX7covsWPuFAW/AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T13:21:08.458015Z","bundle_sha256":"d049db187aaba3541ff0f775e1ddc73271a3e4c74cf0779312181cdd98b2480f"}}