{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WLGMFFBXJKZNXBEI624TMHC5W3","short_pith_number":"pith:WLGMFFBX","canonical_record":{"source":{"id":"1806.04822","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-13T02:16:01Z","cross_cats_sorted":[],"title_canon_sha256":"9d6e4884689a9d0984de3833686deb77c005414cebf836bf685591c2dce8d7be","abstract_canon_sha256":"6788db30c5cd82eb9339c6e13d6e1f9c434fd8fe1965f84c3112906170c682c3"},"schema_version":"1.0"},"canonical_sha256":"b2ccc294374ab2db8488f6b9361c5db6f6686cbed0e16f845970115144c8a4e5","source":{"kind":"arxiv","id":"1806.04822","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.04822","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"arxiv_version","alias_value":"1806.04822v3","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.04822","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"pith_short_12","alias_value":"WLGMFFBXJKZN","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WLGMFFBXJKZNXBEI","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WLGMFFBX","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WLGMFFBXJKZNXBEI624TMHC5W3","target":"record","payload":{"canonical_record":{"source":{"id":"1806.04822","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-13T02:16:01Z","cross_cats_sorted":[],"title_canon_sha256":"9d6e4884689a9d0984de3833686deb77c005414cebf836bf685591c2dce8d7be","abstract_canon_sha256":"6788db30c5cd82eb9339c6e13d6e1f9c434fd8fe1965f84c3112906170c682c3"},"schema_version":"1.0"},"canonical_sha256":"b2ccc294374ab2db8488f6b9361c5db6f6686cbed0e16f845970115144c8a4e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:09.836218Z","signature_b64":"FMsU55MhSWnYSVmzsFTtQFRdS5ja/VSToHyGZA2qth12pkqcbFL7tR4JPWbETh3zrBTmV6+w5CJAYDYK/WrBAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2ccc294374ab2db8488f6b9361c5db6f6686cbed0e16f845970115144c8a4e5","last_reissued_at":"2026-05-18T00:13:09.835600Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:09.835600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.04822","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":"2026-05-18T00:13:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8CE+6LZplY2acQP/H0QldCBmwwP2OJMyvdGn5C9hotejffLud8tZFoMqL8BBifVk6YfQzeC9y26xe2i2FH6jAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:42:18.229751Z"},"content_sha256":"fd4c37ac633d799938a61ab482da4bfc72f6d8aecc2144782217ab90aa3d262f","schema_version":"1.0","event_id":"sha256:fd4c37ac633d799938a61ab482da4bfc72f6d8aecc2144782217ab90aa3d262f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WLGMFFBXJKZNXBEI624TMHC5W3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SGM: Sequence Generation Model for Multi-label Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Houfeng Wang, Pengcheng Yang, Shuming Ma, Wei Li, Wei Wu, Xu Sun","submitted_at":"2018-06-13T02:16:01Z","abstract_excerpt":"Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experime"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.04822","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":"2026-05-18T00:13:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VOy6jLZ5oZgSxSkAa+K+uc/E/le1CkEVFe+dMMKoMfK/fhZsDdXmByf8gBZ2d2Nucou3nKU2GoVY8YGHyuGHBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:42:18.230087Z"},"content_sha256":"fe8f856e23fc7aaf1f001769c17cbca1fe67dbec3cd04353e56d30cb0c7e6c6d","schema_version":"1.0","event_id":"sha256:fe8f856e23fc7aaf1f001769c17cbca1fe67dbec3cd04353e56d30cb0c7e6c6d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WLGMFFBXJKZNXBEI624TMHC5W3/bundle.json","state_url":"https://pith.science/pith/WLGMFFBXJKZNXBEI624TMHC5W3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WLGMFFBXJKZNXBEI624TMHC5W3/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":"2026-06-02T14:42:18Z","links":{"resolver":"https://pith.science/pith/WLGMFFBXJKZNXBEI624TMHC5W3","bundle":"https://pith.science/pith/WLGMFFBXJKZNXBEI624TMHC5W3/bundle.json","state":"https://pith.science/pith/WLGMFFBXJKZNXBEI624TMHC5W3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WLGMFFBXJKZNXBEI624TMHC5W3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WLGMFFBXJKZNXBEI624TMHC5W3","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":"6788db30c5cd82eb9339c6e13d6e1f9c434fd8fe1965f84c3112906170c682c3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-13T02:16:01Z","title_canon_sha256":"9d6e4884689a9d0984de3833686deb77c005414cebf836bf685591c2dce8d7be"},"schema_version":"1.0","source":{"id":"1806.04822","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.04822","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"arxiv_version","alias_value":"1806.04822v3","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.04822","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"pith_short_12","alias_value":"WLGMFFBXJKZN","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WLGMFFBXJKZNXBEI","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WLGMFFBX","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:fe8f856e23fc7aaf1f001769c17cbca1fe67dbec3cd04353e56d30cb0c7e6c6d","target":"graph","created_at":"2026-05-18T00:13:09Z","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":"Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experime","authors_text":"Houfeng Wang, Pengcheng Yang, Shuming Ma, Wei Li, Wei Wu, Xu Sun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-13T02:16:01Z","title":"SGM: Sequence Generation Model for Multi-label Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.04822","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:fd4c37ac633d799938a61ab482da4bfc72f6d8aecc2144782217ab90aa3d262f","target":"record","created_at":"2026-05-18T00:13:09Z","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":"6788db30c5cd82eb9339c6e13d6e1f9c434fd8fe1965f84c3112906170c682c3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-13T02:16:01Z","title_canon_sha256":"9d6e4884689a9d0984de3833686deb77c005414cebf836bf685591c2dce8d7be"},"schema_version":"1.0","source":{"id":"1806.04822","kind":"arxiv","version":3}},"canonical_sha256":"b2ccc294374ab2db8488f6b9361c5db6f6686cbed0e16f845970115144c8a4e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2ccc294374ab2db8488f6b9361c5db6f6686cbed0e16f845970115144c8a4e5","first_computed_at":"2026-05-18T00:13:09.835600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:09.835600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FMsU55MhSWnYSVmzsFTtQFRdS5ja/VSToHyGZA2qth12pkqcbFL7tR4JPWbETh3zrBTmV6+w5CJAYDYK/WrBAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:09.836218Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.04822","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd4c37ac633d799938a61ab482da4bfc72f6d8aecc2144782217ab90aa3d262f","sha256:fe8f856e23fc7aaf1f001769c17cbca1fe67dbec3cd04353e56d30cb0c7e6c6d"],"state_sha256":"fdde775b25b8eb5b19640d15bfec3d89536e9ac371e177d7a08fb80791b51474"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ijbg9YlNoMFfY36yT/w9j32jwyVnVUgEB7TzFdXLtQCnEnKJCh59tT4SPi0oV8HhvBiGutD7qS3MvT/D6u99Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T14:42:18.231894Z","bundle_sha256":"2d9cae41e8b294cc25e265d7e6a85e9855fc40251abf2d49c251093b2a4ca22d"}}