{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:A4PTJDEJ6EBZLN4DJ2GXNPWEKP","short_pith_number":"pith:A4PTJDEJ","canonical_record":{"source":{"id":"1710.07210","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T23:35:58Z","cross_cats_sorted":[],"title_canon_sha256":"c49a1945bac40c56d7c7b4dd426180033fd3ccd866cb7ed1ddfc65713b5002ca","abstract_canon_sha256":"710604108cfc2bb9f5f6d5ea5fecadfdb8f5791fc7d8a70e5743b79f16d7693f"},"schema_version":"1.0"},"canonical_sha256":"071f348c89f10395b7834e8d76bec453f436648213ff0d03a11751163f0c2ca2","source":{"kind":"arxiv","id":"1710.07210","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.07210","created_at":"2026-05-18T00:32:26Z"},{"alias_kind":"arxiv_version","alias_value":"1710.07210v1","created_at":"2026-05-18T00:32:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07210","created_at":"2026-05-18T00:32:26Z"},{"alias_kind":"pith_short_12","alias_value":"A4PTJDEJ6EBZ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"A4PTJDEJ6EBZLN4D","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"A4PTJDEJ","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:A4PTJDEJ6EBZLN4DJ2GXNPWEKP","target":"record","payload":{"canonical_record":{"source":{"id":"1710.07210","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T23:35:58Z","cross_cats_sorted":[],"title_canon_sha256":"c49a1945bac40c56d7c7b4dd426180033fd3ccd866cb7ed1ddfc65713b5002ca","abstract_canon_sha256":"710604108cfc2bb9f5f6d5ea5fecadfdb8f5791fc7d8a70e5743b79f16d7693f"},"schema_version":"1.0"},"canonical_sha256":"071f348c89f10395b7834e8d76bec453f436648213ff0d03a11751163f0c2ca2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:26.587344Z","signature_b64":"Cq9tfyvPwVhDPAEY/aPljCNBjqHw8gL25sGpAybYIxNU8TRtptVIQx5m1P+wRYxeoWvJzBbOK4ttWKKUwiJDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"071f348c89f10395b7834e8d76bec453f436648213ff0d03a11751163f0c2ca2","last_reissued_at":"2026-05-18T00:32:26.586638Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:26.586638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.07210","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":"2026-05-18T00:32:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tmRjRug0LijvSqLA8geaFMJf4LRVQL6rPQ4ZBPoGaHfJZ47glpjjUot52bzUCtDu2V/pwHAeGmaa+4v+s3kGAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T08:32:15.329164Z"},"content_sha256":"33a0fc5390d5dea4dbeef1897bb5222e3980e3e545660e3e15cddfe5d16e6dfe","schema_version":"1.0","event_id":"sha256:33a0fc5390d5dea4dbeef1897bb5222e3980e3e545660e3e15cddfe5d16e6dfe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:A4PTJDEJ6EBZLN4DJ2GXNPWEKP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Task Label Embedding for Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yaohui Jin, Yongkun Wang","submitted_at":"2017-10-17T23:35:58Z","abstract_excerpt":"Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07210","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":"2026-05-18T00:32:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0I3lD0e13u7x62wy2I1AwmQuHhsKnAQ+0qgwHRrnFshiM7eTSmGP+G/7nQlRoXKKN5iAaVjQ/C9q63lo0ibaAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T08:32:15.329523Z"},"content_sha256":"89f337f725647ada3c39995a977e454a2bc618ddb65f43869683793bf27c5420","schema_version":"1.0","event_id":"sha256:89f337f725647ada3c39995a977e454a2bc618ddb65f43869683793bf27c5420"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP/bundle.json","state_url":"https://pith.science/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP/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":"2026-06-02T08:32:15Z","links":{"resolver":"https://pith.science/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP","bundle":"https://pith.science/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP/bundle.json","state":"https://pith.science/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A4PTJDEJ6EBZLN4DJ2GXNPWEKP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:A4PTJDEJ6EBZLN4DJ2GXNPWEKP","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":"710604108cfc2bb9f5f6d5ea5fecadfdb8f5791fc7d8a70e5743b79f16d7693f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T23:35:58Z","title_canon_sha256":"c49a1945bac40c56d7c7b4dd426180033fd3ccd866cb7ed1ddfc65713b5002ca"},"schema_version":"1.0","source":{"id":"1710.07210","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.07210","created_at":"2026-05-18T00:32:26Z"},{"alias_kind":"arxiv_version","alias_value":"1710.07210v1","created_at":"2026-05-18T00:32:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07210","created_at":"2026-05-18T00:32:26Z"},{"alias_kind":"pith_short_12","alias_value":"A4PTJDEJ6EBZ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"A4PTJDEJ6EBZLN4D","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"A4PTJDEJ","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:89f337f725647ada3c39995a977e454a2bc618ddb65f43869683793bf27c5420","target":"graph","created_at":"2026-05-18T00:32:26Z","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-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and se","authors_text":"Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yaohui Jin, Yongkun Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T23:35:58Z","title":"Multi-Task Label Embedding for Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07210","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":"sha256:33a0fc5390d5dea4dbeef1897bb5222e3980e3e545660e3e15cddfe5d16e6dfe","target":"record","created_at":"2026-05-18T00:32:26Z","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":"710604108cfc2bb9f5f6d5ea5fecadfdb8f5791fc7d8a70e5743b79f16d7693f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T23:35:58Z","title_canon_sha256":"c49a1945bac40c56d7c7b4dd426180033fd3ccd866cb7ed1ddfc65713b5002ca"},"schema_version":"1.0","source":{"id":"1710.07210","kind":"arxiv","version":1}},"canonical_sha256":"071f348c89f10395b7834e8d76bec453f436648213ff0d03a11751163f0c2ca2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"071f348c89f10395b7834e8d76bec453f436648213ff0d03a11751163f0c2ca2","first_computed_at":"2026-05-18T00:32:26.586638Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:26.586638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Cq9tfyvPwVhDPAEY/aPljCNBjqHw8gL25sGpAybYIxNU8TRtptVIQx5m1P+wRYxeoWvJzBbOK4ttWKKUwiJDBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:26.587344Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.07210","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33a0fc5390d5dea4dbeef1897bb5222e3980e3e545660e3e15cddfe5d16e6dfe","sha256:89f337f725647ada3c39995a977e454a2bc618ddb65f43869683793bf27c5420"],"state_sha256":"08ed2e837229e39c4dc48d2669a28b0a7d357b5c45a01d85290cccff6413a895"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1K4IqyMFl36j+wmK+h/FOzVDF7A0qmEbHeimche7nZLrg5fszWFOePYtYxtFD4uPcTLgaO6+CVrGq+1lm7MJCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T08:32:15.331446Z","bundle_sha256":"3e5b3a1e28c63a8e6dbc353add143ff146850a98202ee6ff13b70ef7c822a676"}}