{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:UQ2CLZZCYLVYUV5CYLUNBDWZAC","short_pith_number":"pith:UQ2CLZZC","canonical_record":{"source":{"id":"1706.06714","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-21T01:07:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ee7aa3c7f130506dacdcd3b6ed9ab3b7a635f4841c5e9c1d56825ac21ede89ec","abstract_canon_sha256":"0c08c6ac368a0d212697a3d301d6823ab49d4cc5c9ba6e33ee82ef4657f45320"},"schema_version":"1.0"},"canonical_sha256":"a43425e722c2eb8a57a2c2e8d08ed900ab0012ff618fea28ed9af3f1d7e20fc4","source":{"kind":"arxiv","id":"1706.06714","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.06714","created_at":"2026-05-18T00:40:29Z"},{"alias_kind":"arxiv_version","alias_value":"1706.06714v3","created_at":"2026-05-18T00:40:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06714","created_at":"2026-05-18T00:40:29Z"},{"alias_kind":"pith_short_12","alias_value":"UQ2CLZZCYLVY","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UQ2CLZZCYLVYUV5C","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UQ2CLZZC","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:UQ2CLZZCYLVYUV5CYLUNBDWZAC","target":"record","payload":{"canonical_record":{"source":{"id":"1706.06714","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-21T01:07:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ee7aa3c7f130506dacdcd3b6ed9ab3b7a635f4841c5e9c1d56825ac21ede89ec","abstract_canon_sha256":"0c08c6ac368a0d212697a3d301d6823ab49d4cc5c9ba6e33ee82ef4657f45320"},"schema_version":"1.0"},"canonical_sha256":"a43425e722c2eb8a57a2c2e8d08ed900ab0012ff618fea28ed9af3f1d7e20fc4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:29.843253Z","signature_b64":"fNbC0kTA/Qbd5d220EJyB4I3CcNEnxCqTkbw2gKzmA9En41w8liatrX5U273pZhxWgNtQTH8rTlVfVkDuzKACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a43425e722c2eb8a57a2c2e8d08ed900ab0012ff618fea28ed9af3f1d7e20fc4","last_reissued_at":"2026-05-18T00:40:29.842865Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:29.842865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.06714","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:40:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D/4qsdavAC/7nvJ1ISqT8oqe2O49shjWMjdi4qpJdWNVVcBfDWngs1cpbzd3XD9cL1zidS7kyUPWoOJCdE/BBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:47:58.392202Z"},"content_sha256":"0ca4fc8176a66f43d29c1a314edac25ccec029f00a239f5698ec92b35d0b9b95","schema_version":"1.0","event_id":"sha256:0ca4fc8176a66f43d29c1a314edac25ccec029f00a239f5698ec92b35d0b9b95"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:UQ2CLZZCYLVYUV5CYLUNBDWZAC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Le-Minh Nguyen, Van-Khanh Tran","submitted_at":"2017-06-21T01:07:02Z","abstract_excerpt":"Natural language generation (NLG) is an important component in spoken dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements. The proposed model can be joi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06714","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:40:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ulZscPitv6EgEh31dD1xqZOPaBH9FRkpoidT7MrlaeN5dFGsG2Eybl7Ah4SUCPMQSW/NFF8SE/qSRFLw4uSAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:47:58.392866Z"},"content_sha256":"3fb93fa442a437d5327901fcd5f547f7ee0a18b24a052e4ab3aabc9aa08d79c8","schema_version":"1.0","event_id":"sha256:3fb93fa442a437d5327901fcd5f547f7ee0a18b24a052e4ab3aabc9aa08d79c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC/bundle.json","state_url":"https://pith.science/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC/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":"2026-06-07T14:47:58Z","links":{"resolver":"https://pith.science/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC","bundle":"https://pith.science/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC/bundle.json","state":"https://pith.science/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UQ2CLZZCYLVYUV5CYLUNBDWZAC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:UQ2CLZZCYLVYUV5CYLUNBDWZAC","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":"0c08c6ac368a0d212697a3d301d6823ab49d4cc5c9ba6e33ee82ef4657f45320","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-21T01:07:02Z","title_canon_sha256":"ee7aa3c7f130506dacdcd3b6ed9ab3b7a635f4841c5e9c1d56825ac21ede89ec"},"schema_version":"1.0","source":{"id":"1706.06714","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.06714","created_at":"2026-05-18T00:40:29Z"},{"alias_kind":"arxiv_version","alias_value":"1706.06714v3","created_at":"2026-05-18T00:40:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06714","created_at":"2026-05-18T00:40:29Z"},{"alias_kind":"pith_short_12","alias_value":"UQ2CLZZCYLVY","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UQ2CLZZCYLVYUV5C","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UQ2CLZZC","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:3fb93fa442a437d5327901fcd5f547f7ee0a18b24a052e4ab3aabc9aa08d79c8","target":"graph","created_at":"2026-05-18T00:40:29Z","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 an important component in spoken dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements. The proposed model can be joi","authors_text":"Le-Minh Nguyen, Van-Khanh Tran","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-21T01:07:02Z","title":"Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06714","kind":"arxiv","version":3},"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":"sha256:0ca4fc8176a66f43d29c1a314edac25ccec029f00a239f5698ec92b35d0b9b95","target":"record","created_at":"2026-05-18T00:40:29Z","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":"0c08c6ac368a0d212697a3d301d6823ab49d4cc5c9ba6e33ee82ef4657f45320","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-21T01:07:02Z","title_canon_sha256":"ee7aa3c7f130506dacdcd3b6ed9ab3b7a635f4841c5e9c1d56825ac21ede89ec"},"schema_version":"1.0","source":{"id":"1706.06714","kind":"arxiv","version":3}},"canonical_sha256":"a43425e722c2eb8a57a2c2e8d08ed900ab0012ff618fea28ed9af3f1d7e20fc4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a43425e722c2eb8a57a2c2e8d08ed900ab0012ff618fea28ed9af3f1d7e20fc4","first_computed_at":"2026-05-18T00:40:29.842865Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:40:29.842865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fNbC0kTA/Qbd5d220EJyB4I3CcNEnxCqTkbw2gKzmA9En41w8liatrX5U273pZhxWgNtQTH8rTlVfVkDuzKACw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:40:29.843253Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.06714","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ca4fc8176a66f43d29c1a314edac25ccec029f00a239f5698ec92b35d0b9b95","sha256:3fb93fa442a437d5327901fcd5f547f7ee0a18b24a052e4ab3aabc9aa08d79c8"],"state_sha256":"641f100715af81365e48e445520de35d6a2c8e4a6e7c0435663c2e0bfe267ff9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZvtLZuNrdaJKq97nUsF6wKL4gfldilkHProZLahnpkBIFCc6jWs37ltLJi44eaVO//xKsgY2QZO9568o55YbDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T14:47:58.398187Z","bundle_sha256":"92a42dcc3dc25e58d7b0e968dd47e9d2109c841d71ac9f8907417e12096f8e88"}}