{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6JWLSM7W2XU7RKFYAIKO34IEXV","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":"0d039f03b493ed648c9659a25cd49e11232026cca5f862431b3bc9c0267d4377","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-28T11:52:16Z","title_canon_sha256":"39c7a38fecea9a2574d9294f2dab04fece8cc97e767df402b31748257f3af727"},"schema_version":"1.0","source":{"id":"2211.07716","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07716","created_at":"2026-07-05T05:16:05Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07716v1","created_at":"2026-07-05T05:16:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07716","created_at":"2026-07-05T05:16:05Z"},{"alias_kind":"pith_short_12","alias_value":"6JWLSM7W2XU7","created_at":"2026-07-05T05:16:05Z"},{"alias_kind":"pith_short_16","alias_value":"6JWLSM7W2XU7RKFY","created_at":"2026-07-05T05:16:05Z"},{"alias_kind":"pith_short_8","alias_value":"6JWLSM7W","created_at":"2026-07-05T05:16:05Z"}],"graph_snapshots":[{"event_id":"sha256:a16fef203511e09610ba0d85e24bcfe08b9bebd0390b6283648378a39a38065d","target":"graph","created_at":"2026-07-05T05:16:05Z","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/2211.07716/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Natural language processing methods have several applications in automated auditing, including document or passage classification, information retrieval, and question answering. However, training such models requires a large amount of annotated data which is scarce in industrial settings. At the same time, techniques like zero-shot and unsupervised learning allow for application of models pre-trained using general domain data to unseen domains.\n  In this work, we study the efficiency of unsupervised text matching using Sentence-Bert, a transformer-based model, by applying it to the semantic si","authors_text":"Bernd Kliem, David Biesner, Maren Pielka, Rafet Sifa, Rajkumar Ramamurthy, R\\\"udiger Loitz, Tim Dilmaghani","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-28T11:52:16Z","title":"Zero-Shot Text Matching for Automated Auditing using Sentence Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07716","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:ea664256aef7e1f0a4781a79b0509b235f714f45d36495c035113c082e47cc02","target":"record","created_at":"2026-07-05T05:16:05Z","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":"0d039f03b493ed648c9659a25cd49e11232026cca5f862431b3bc9c0267d4377","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-28T11:52:16Z","title_canon_sha256":"39c7a38fecea9a2574d9294f2dab04fece8cc97e767df402b31748257f3af727"},"schema_version":"1.0","source":{"id":"2211.07716","kind":"arxiv","version":1}},"canonical_sha256":"f26cb933f6d5e9f8a8b80214edf104bd6295a3786550dae73d135a984509d1d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f26cb933f6d5e9f8a8b80214edf104bd6295a3786550dae73d135a984509d1d6","first_computed_at":"2026-07-05T05:16:05.332834Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:16:05.332834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JZ/hT8wiGttyQpOBwIozr2Eem9JrfLrq4FmCg1Uz0U0YegEjPD1Rn58wDoUVnoEUXtwlpjq9CBOSgICAlG2+Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:16:05.333272Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.07716","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea664256aef7e1f0a4781a79b0509b235f714f45d36495c035113c082e47cc02","sha256:a16fef203511e09610ba0d85e24bcfe08b9bebd0390b6283648378a39a38065d"],"state_sha256":"483e0ac38c857b8069d0583267a30eb1e43f17a0531fb6dcd3e30c00e8ca5eb2"}