{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:F3ABSF5OETUKNE6E4RCJO4KNAJ","short_pith_number":"pith:F3ABSF5O","canonical_record":{"source":{"id":"1908.06136","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-16T19:31:13Z","cross_cats_sorted":[],"title_canon_sha256":"0dcdb85e94ae9f48cb74c84448ab22a29ef44c4c534ee0188ad0fedc0e4a7e97","abstract_canon_sha256":"566bbed50e33d11b8eb0126def5fc0f2d33aca5a8867f2dd6eceed0972887f2e"},"schema_version":"1.0"},"canonical_sha256":"2ec01917ae24e8a693c4e44497714d025838329a9e4749444a9bcbdbff66f8ed","source":{"kind":"arxiv","id":"1908.06136","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06136","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06136v2","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06136","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"pith_short_12","alias_value":"F3ABSF5OETUK","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"pith_short_16","alias_value":"F3ABSF5OETUKNE6E","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"pith_short_8","alias_value":"F3ABSF5O","created_at":"2026-07-05T00:06:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:F3ABSF5OETUKNE6E4RCJO4KNAJ","target":"record","payload":{"canonical_record":{"source":{"id":"1908.06136","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-16T19:31:13Z","cross_cats_sorted":[],"title_canon_sha256":"0dcdb85e94ae9f48cb74c84448ab22a29ef44c4c534ee0188ad0fedc0e4a7e97","abstract_canon_sha256":"566bbed50e33d11b8eb0126def5fc0f2d33aca5a8867f2dd6eceed0972887f2e"},"schema_version":"1.0"},"canonical_sha256":"2ec01917ae24e8a693c4e44497714d025838329a9e4749444a9bcbdbff66f8ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:06:10.523680Z","signature_b64":"xWy2Q2+ksNA8b073SFXA97s7Aw7bpHMpBlMsxuEmLRImDsYvM+jiZRuskxMWXKaA6M+tGkmJPu1MzmCBOmgvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ec01917ae24e8a693c4e44497714d025838329a9e4749444a9bcbdbff66f8ed","last_reissued_at":"2026-07-05T00:06:10.523221Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:06:10.523221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.06136","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:06:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/LSI3LAb3FZ53rtJ1K0yOsj3w6wQO4fw2G5V/5rQ0fCPECsVoqjQ6ldYgV06ZjHW4umlbbjKCXWfLTNYinnVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T08:26:28.532156Z"},"content_sha256":"e616bc0fdc2bc0fb30dc2d843356272c9063c1d0af987ac86314b07fb920c836","schema_version":"1.0","event_id":"sha256:e616bc0fdc2bc0fb30dc2d843356272c9063c1d0af987ac86314b07fb920c836"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:F3ABSF5OETUKNE6E4RCJO4KNAJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transductive Auxiliary Task Self-Training for Neural Multi-Task Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Isabelle Augenstein, Johannes Bjerva, Katharina Kann","submitted_at":"2019-08-16T19:31:13Z","abstract_excerpt":"Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06136","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.06136/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:06:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8O/ygwGVkkTvshvPwm2u+VLS6ifFdJOsIfkDL0t4W9lXP219vM9QAnKBrjbYPVEQyVo0FeK2QGhLnak/3aLFAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T08:26:28.533084Z"},"content_sha256":"1312f37a0b2df9621dd67f6f4bb3636b969c50d83abdaf71e56971cea8db993c","schema_version":"1.0","event_id":"sha256:1312f37a0b2df9621dd67f6f4bb3636b969c50d83abdaf71e56971cea8db993c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ/bundle.json","state_url":"https://pith.science/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ/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":"2026-08-16T08:26:28Z","links":{"resolver":"https://pith.science/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ","bundle":"https://pith.science/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ/bundle.json","state":"https://pith.science/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F3ABSF5OETUKNE6E4RCJO4KNAJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:F3ABSF5OETUKNE6E4RCJO4KNAJ","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":"566bbed50e33d11b8eb0126def5fc0f2d33aca5a8867f2dd6eceed0972887f2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-16T19:31:13Z","title_canon_sha256":"0dcdb85e94ae9f48cb74c84448ab22a29ef44c4c534ee0188ad0fedc0e4a7e97"},"schema_version":"1.0","source":{"id":"1908.06136","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06136","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06136v2","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06136","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"pith_short_12","alias_value":"F3ABSF5OETUK","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"pith_short_16","alias_value":"F3ABSF5OETUKNE6E","created_at":"2026-07-05T00:06:10Z"},{"alias_kind":"pith_short_8","alias_value":"F3ABSF5O","created_at":"2026-07-05T00:06:10Z"}],"graph_snapshots":[{"event_id":"sha256:1312f37a0b2df9621dd67f6f4bb3636b969c50d83abdaf71e56971cea8db993c","target":"graph","created_at":"2026-07-05T00:06:10Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.06136/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task","authors_text":"Isabelle Augenstein, Johannes Bjerva, Katharina Kann","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-16T19:31:13Z","title":"Transductive Auxiliary Task Self-Training for Neural Multi-Task Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06136","kind":"arxiv","version":2},"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:e616bc0fdc2bc0fb30dc2d843356272c9063c1d0af987ac86314b07fb920c836","target":"record","created_at":"2026-07-05T00:06:10Z","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":"566bbed50e33d11b8eb0126def5fc0f2d33aca5a8867f2dd6eceed0972887f2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-16T19:31:13Z","title_canon_sha256":"0dcdb85e94ae9f48cb74c84448ab22a29ef44c4c534ee0188ad0fedc0e4a7e97"},"schema_version":"1.0","source":{"id":"1908.06136","kind":"arxiv","version":2}},"canonical_sha256":"2ec01917ae24e8a693c4e44497714d025838329a9e4749444a9bcbdbff66f8ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ec01917ae24e8a693c4e44497714d025838329a9e4749444a9bcbdbff66f8ed","first_computed_at":"2026-07-05T00:06:10.523221Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:06:10.523221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xWy2Q2+ksNA8b073SFXA97s7Aw7bpHMpBlMsxuEmLRImDsYvM+jiZRuskxMWXKaA6M+tGkmJPu1MzmCBOmgvDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:06:10.523680Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.06136","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e616bc0fdc2bc0fb30dc2d843356272c9063c1d0af987ac86314b07fb920c836","sha256:1312f37a0b2df9621dd67f6f4bb3636b969c50d83abdaf71e56971cea8db993c"],"state_sha256":"f772b6e7a0b10c421b1acb0bbe3329f07441846127cdb2b7fa5ba37574ee41f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y9JP2pswdNo+nwF2xl6y8GNFZMBtQNfqxef8WaYbCxbvrZDwmQYCAd6GUtqfOcVqdyh2/6oTbe0xsHF8JvA+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T08:26:28.537448Z","bundle_sha256":"e1ae2b53425b258aa1319aab6dfb20daaeb8709490d7f68f4fd1e47825e243ed"}}