{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:NRUINYG6V552CE7BWN2ASNR763","short_pith_number":"pith:NRUINYG6","canonical_record":{"source":{"id":"1508.01755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-07T16:34:11Z","cross_cats_sorted":[],"title_canon_sha256":"c943fd06088a4cc14794cba70ca65290804b06b0d49680ac3bbf8bde4dacc06c","abstract_canon_sha256":"86a6415020f18dc3e8194d45038237e9566355fdf15a8f70d630102e39eecdeb"},"schema_version":"1.0"},"canonical_sha256":"6c6886e0deaf7ba113e1b37409363ff6dd6c6fc667b069015c6e852af9de34ea","source":{"kind":"arxiv","id":"1508.01755","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.01755","created_at":"2026-05-18T01:35:38Z"},{"alias_kind":"arxiv_version","alias_value":"1508.01755v1","created_at":"2026-05-18T01:35:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01755","created_at":"2026-05-18T01:35:38Z"},{"alias_kind":"pith_short_12","alias_value":"NRUINYG6V552","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"NRUINYG6V552CE7B","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"NRUINYG6","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:NRUINYG6V552CE7BWN2ASNR763","target":"record","payload":{"canonical_record":{"source":{"id":"1508.01755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-07T16:34:11Z","cross_cats_sorted":[],"title_canon_sha256":"c943fd06088a4cc14794cba70ca65290804b06b0d49680ac3bbf8bde4dacc06c","abstract_canon_sha256":"86a6415020f18dc3e8194d45038237e9566355fdf15a8f70d630102e39eecdeb"},"schema_version":"1.0"},"canonical_sha256":"6c6886e0deaf7ba113e1b37409363ff6dd6c6fc667b069015c6e852af9de34ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:38.822669Z","signature_b64":"4krjHbylm++jfSj/0lVIVqG0fjJKwsJcW2cLVDucz4O7KJqv/xFrZ+Be4z7Hz13RvNt2SKs92kjFfzXZSwxFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c6886e0deaf7ba113e1b37409363ff6dd6c6fc667b069015c6e852af9de34ea","last_reissued_at":"2026-05-18T01:35:38.821886Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:38.821886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1508.01755","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:35:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9zrd5VSOmSEviQu0C1f4T7b79W8BT1BK0p7hRJOAUMFmJ1DZtLYQFF+F7xe3T1pOs100LO2SxjE+u/b/T3GhAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T01:07:33.533417Z"},"content_sha256":"acefea487c716cb3c4d497d95f768ede1b18e86897e90d04ae432ba412b1fb18","schema_version":"1.0","event_id":"sha256:acefea487c716cb3c4d497d95f768ede1b18e86897e90d04ae432ba412b1fb18"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:NRUINYG6V552CE7BWN2ASNR763","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"David Vandyke, Dongho Kim, Milica Gasic, Nikola Mrksic, Pei-Hao Su, Steve Young, Tsung-Hsien Wen","submitted_at":"2015-08-07T16:34:11Z","abstract_excerpt":"The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01755","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:35:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c3JSXHpctBl2jF8STnoV//PZWp5ckdHRCdxXeuj9v8O7TGJOWn3gcpdz1h6FSMq5zWilvXZWi67N2o2fiHmIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T01:07:33.533782Z"},"content_sha256":"4f763577f52a777fc2b3c2c3601a6a0ebd522f3085a969ba53b0dcc8344a91a3","schema_version":"1.0","event_id":"sha256:4f763577f52a777fc2b3c2c3601a6a0ebd522f3085a969ba53b0dcc8344a91a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NRUINYG6V552CE7BWN2ASNR763/bundle.json","state_url":"https://pith.science/pith/NRUINYG6V552CE7BWN2ASNR763/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NRUINYG6V552CE7BWN2ASNR763/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":"2026-05-20T01:07:33Z","links":{"resolver":"https://pith.science/pith/NRUINYG6V552CE7BWN2ASNR763","bundle":"https://pith.science/pith/NRUINYG6V552CE7BWN2ASNR763/bundle.json","state":"https://pith.science/pith/NRUINYG6V552CE7BWN2ASNR763/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NRUINYG6V552CE7BWN2ASNR763/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:NRUINYG6V552CE7BWN2ASNR763","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":"86a6415020f18dc3e8194d45038237e9566355fdf15a8f70d630102e39eecdeb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-07T16:34:11Z","title_canon_sha256":"c943fd06088a4cc14794cba70ca65290804b06b0d49680ac3bbf8bde4dacc06c"},"schema_version":"1.0","source":{"id":"1508.01755","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.01755","created_at":"2026-05-18T01:35:38Z"},{"alias_kind":"arxiv_version","alias_value":"1508.01755v1","created_at":"2026-05-18T01:35:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01755","created_at":"2026-05-18T01:35:38Z"},{"alias_kind":"pith_short_12","alias_value":"NRUINYG6V552","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"NRUINYG6V552CE7B","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"NRUINYG6","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:4f763577f52a777fc2b3c2c3601a6a0ebd522f3085a969ba53b0dcc8344a91a3","target":"graph","created_at":"2026-05-18T01:35:38Z","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":"The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be ","authors_text":"David Vandyke, Dongho Kim, Milica Gasic, Nikola Mrksic, Pei-Hao Su, Steve Young, Tsung-Hsien Wen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-07T16:34:11Z","title":"Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01755","kind":"arxiv","version":1},"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:acefea487c716cb3c4d497d95f768ede1b18e86897e90d04ae432ba412b1fb18","target":"record","created_at":"2026-05-18T01:35:38Z","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":"86a6415020f18dc3e8194d45038237e9566355fdf15a8f70d630102e39eecdeb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-07T16:34:11Z","title_canon_sha256":"c943fd06088a4cc14794cba70ca65290804b06b0d49680ac3bbf8bde4dacc06c"},"schema_version":"1.0","source":{"id":"1508.01755","kind":"arxiv","version":1}},"canonical_sha256":"6c6886e0deaf7ba113e1b37409363ff6dd6c6fc667b069015c6e852af9de34ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c6886e0deaf7ba113e1b37409363ff6dd6c6fc667b069015c6e852af9de34ea","first_computed_at":"2026-05-18T01:35:38.821886Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:35:38.821886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4krjHbylm++jfSj/0lVIVqG0fjJKwsJcW2cLVDucz4O7KJqv/xFrZ+Be4z7Hz13RvNt2SKs92kjFfzXZSwxFCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:35:38.822669Z","signed_message":"canonical_sha256_bytes"},"source_id":"1508.01755","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:acefea487c716cb3c4d497d95f768ede1b18e86897e90d04ae432ba412b1fb18","sha256:4f763577f52a777fc2b3c2c3601a6a0ebd522f3085a969ba53b0dcc8344a91a3"],"state_sha256":"dbe2680c0e41a385471a15224124f8b2c394c85660715ea610dd95afeea72162"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"URDLUiFrYPiTIfrtE3gF58DjK1ZXkVjmtT5uGCZcWGK6X+p7nL7TbfuspyNI2fkRAUE032RfgkVKxAKv8//UDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T01:07:33.535826Z","bundle_sha256":"8bc175467c79de062513da7f496d5e385e0c81c95a1efa3f892276ccdd70b8a0"}}