{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:62HY5N64NH25MCPES4V7XACOJJ","short_pith_number":"pith:62HY5N64","canonical_record":{"source":{"id":"1906.06481","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-15T06:58:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9d15b59c3d2f3436b4f2f5ec3e21dff1c7989a2ee62a3d2858fa13141f103d72","abstract_canon_sha256":"3a78ff621ef8a840d7fa3d446ed4da1d34baf7bc7c0d3170bb4fb924271133fb"},"schema_version":"1.0"},"canonical_sha256":"f68f8eb7dc69f5d609e4972bfb804e4a7d82cdc9f24c3e3f6f9c41e672d5d3a3","source":{"kind":"arxiv","id":"1906.06481","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06481","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06481v1","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06481","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"pith_short_12","alias_value":"62HY5N64NH25","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"62HY5N64NH25MCPE","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"62HY5N64","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:62HY5N64NH25MCPES4V7XACOJJ","target":"record","payload":{"canonical_record":{"source":{"id":"1906.06481","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-15T06:58:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9d15b59c3d2f3436b4f2f5ec3e21dff1c7989a2ee62a3d2858fa13141f103d72","abstract_canon_sha256":"3a78ff621ef8a840d7fa3d446ed4da1d34baf7bc7c0d3170bb4fb924271133fb"},"schema_version":"1.0"},"canonical_sha256":"f68f8eb7dc69f5d609e4972bfb804e4a7d82cdc9f24c3e3f6f9c41e672d5d3a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:13.562485Z","signature_b64":"KeJ9PS4sP+1LiWlkQirJrpDxT9K+HBJV3qyj3o0Ty0E6/mSrraZGeq6VQ5RdGfTwISOQGyyqdsoClqhYQYvYCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f68f8eb7dc69f5d609e4972bfb804e4a7d82cdc9f24c3e3f6f9c41e672d5d3a3","last_reissued_at":"2026-05-17T23:43:13.562079Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:13.562079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.06481","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":"2026-05-17T23:43:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+XZyQoiXtF8AKtO8c7MqdLT+gXG5TgBpH7xRuPsiZ+C1qUXmHZa9JmECdpKKiD3PNM9Q0yNX/iO+EqUzyS0AAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:12:57.019975Z"},"content_sha256":"3c8ade2561a17e9eb566d2b9c4d132f23d30fc13c656c49c3910265fc46fa9b4","schema_version":"1.0","event_id":"sha256:3c8ade2561a17e9eb566d2b9c4d132f23d30fc13c656c49c3910265fc46fa9b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:62HY5N64NH25MCPES4V7XACOJJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Hierarchical Attention Based Seq2seq Model for Chinese Lyrics Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Bojin Zhuang, Haoshen Fan, Jie Wang, Jing Xiao, Shaojun Wang","submitted_at":"2019-06-15T06:58:42Z","abstract_excerpt":"In this paper, we comprehensively study on context-aware generation of Chinese song lyrics. Conventional text generative models generate a sequence or sentence word by word, failing to consider the contextual relationship between sentences. Taking account into the characteristics of lyrics, a hierarchical attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese lyrics generation. With encoding of word-level and sentence-level contextual information, this model promotes the topic relevance and consistency of generation. A large Chinese lyrics corpus is also leveraged for mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06481","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":"2026-05-17T23:43:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1bty+3gjVfa8nNVne2cFzrNN78ns2oXnbCaLQKBWoaoMK9MN1JUzahbm1eQ2k58bYWvbG5ccZp2Gze0kl8HpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:12:57.020341Z"},"content_sha256":"d49f715c8d048817575d3c7f801d30fc42bc2478a67121e2dea5b7e2198edd34","schema_version":"1.0","event_id":"sha256:d49f715c8d048817575d3c7f801d30fc42bc2478a67121e2dea5b7e2198edd34"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/62HY5N64NH25MCPES4V7XACOJJ/bundle.json","state_url":"https://pith.science/pith/62HY5N64NH25MCPES4V7XACOJJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/62HY5N64NH25MCPES4V7XACOJJ/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":"2026-05-27T16:12:57Z","links":{"resolver":"https://pith.science/pith/62HY5N64NH25MCPES4V7XACOJJ","bundle":"https://pith.science/pith/62HY5N64NH25MCPES4V7XACOJJ/bundle.json","state":"https://pith.science/pith/62HY5N64NH25MCPES4V7XACOJJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/62HY5N64NH25MCPES4V7XACOJJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:62HY5N64NH25MCPES4V7XACOJJ","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":"3a78ff621ef8a840d7fa3d446ed4da1d34baf7bc7c0d3170bb4fb924271133fb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-15T06:58:42Z","title_canon_sha256":"9d15b59c3d2f3436b4f2f5ec3e21dff1c7989a2ee62a3d2858fa13141f103d72"},"schema_version":"1.0","source":{"id":"1906.06481","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06481","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06481v1","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06481","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"pith_short_12","alias_value":"62HY5N64NH25","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"62HY5N64NH25MCPE","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"62HY5N64","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:d49f715c8d048817575d3c7f801d30fc42bc2478a67121e2dea5b7e2198edd34","target":"graph","created_at":"2026-05-17T23:43:13Z","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":"In this paper, we comprehensively study on context-aware generation of Chinese song lyrics. Conventional text generative models generate a sequence or sentence word by word, failing to consider the contextual relationship between sentences. Taking account into the characteristics of lyrics, a hierarchical attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese lyrics generation. With encoding of word-level and sentence-level contextual information, this model promotes the topic relevance and consistency of generation. A large Chinese lyrics corpus is also leveraged for mod","authors_text":"Bojin Zhuang, Haoshen Fan, Jie Wang, Jing Xiao, Shaojun Wang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-15T06:58:42Z","title":"A Hierarchical Attention Based Seq2seq Model for Chinese Lyrics Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06481","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":"sha256:3c8ade2561a17e9eb566d2b9c4d132f23d30fc13c656c49c3910265fc46fa9b4","target":"record","created_at":"2026-05-17T23:43:13Z","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":"3a78ff621ef8a840d7fa3d446ed4da1d34baf7bc7c0d3170bb4fb924271133fb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-15T06:58:42Z","title_canon_sha256":"9d15b59c3d2f3436b4f2f5ec3e21dff1c7989a2ee62a3d2858fa13141f103d72"},"schema_version":"1.0","source":{"id":"1906.06481","kind":"arxiv","version":1}},"canonical_sha256":"f68f8eb7dc69f5d609e4972bfb804e4a7d82cdc9f24c3e3f6f9c41e672d5d3a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f68f8eb7dc69f5d609e4972bfb804e4a7d82cdc9f24c3e3f6f9c41e672d5d3a3","first_computed_at":"2026-05-17T23:43:13.562079Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:13.562079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KeJ9PS4sP+1LiWlkQirJrpDxT9K+HBJV3qyj3o0Ty0E6/mSrraZGeq6VQ5RdGfTwISOQGyyqdsoClqhYQYvYCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:13.562485Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.06481","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c8ade2561a17e9eb566d2b9c4d132f23d30fc13c656c49c3910265fc46fa9b4","sha256:d49f715c8d048817575d3c7f801d30fc42bc2478a67121e2dea5b7e2198edd34"],"state_sha256":"78b32e31f260fab450489faad8609832c56be089576f162a89a01fafde851b1c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LrTstE3+MqVBRUQBs1GLkyJaJ6f5m48SsY5h8qaeSl9ksTLJ+o80Ii493kstJ/pT3SfycCjrDtDTPuHukTwoCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T16:12:57.022444Z","bundle_sha256":"80e0a292c424ab78bf716a4475af26a73d9e802a06543bca47a7156e60a121a0"}}