{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:REZ627GJJ33OJVLMXNPP2D2XAF","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":"fef6a7860e2b480f5f435090b3f77c48414502d9d480ca35dfab00bd0e0110b5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T12:33:38Z","title_canon_sha256":"7980f38b9ed697cfcbda4376e89c056098aa5448454473c2c0b37128ac5faee6"},"schema_version":"1.0","source":{"id":"2203.10315","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.10315","created_at":"2026-07-05T04:06:51Z"},{"alias_kind":"arxiv_version","alias_value":"2203.10315v1","created_at":"2026-07-05T04:06:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10315","created_at":"2026-07-05T04:06:51Z"},{"alias_kind":"pith_short_12","alias_value":"REZ627GJJ33O","created_at":"2026-07-05T04:06:51Z"},{"alias_kind":"pith_short_16","alias_value":"REZ627GJJ33OJVLM","created_at":"2026-07-05T04:06:51Z"},{"alias_kind":"pith_short_8","alias_value":"REZ627GJ","created_at":"2026-07-05T04:06:51Z"}],"graph_snapshots":[{"event_id":"sha256:ba3c015af77665357f447f2deab5d140cd9f0ad3924a22f3e709331910675c8f","target":"graph","created_at":"2026-07-05T04:06:51Z","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/2203.10315/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks. But, in the unsupervised POS tagging task, works utilizing PLMs are few and fail to achieve state-of-the-art (SOTA) performance. The recent SOTA performance is yielded by a Guassian HMM variant proposed by He et al. (2018). However, as a generative model, HMM makes very strong independence assumptions, making it very challenging to incorporate contexualized word representations from PLMs. In this work, we for the first time propose a neural conditional random field autoencoder (C","authors_text":"Houquan Zhou, Min Zhang, Yang Li, Zhenghua Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T12:33:38Z","title":"Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10315","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":"sha256:86be76af5f98ee7016db969dcf1325e1be9e4fa2c1fa60c8740660fde0c73467","target":"record","created_at":"2026-07-05T04:06:51Z","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":"fef6a7860e2b480f5f435090b3f77c48414502d9d480ca35dfab00bd0e0110b5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-19T12:33:38Z","title_canon_sha256":"7980f38b9ed697cfcbda4376e89c056098aa5448454473c2c0b37128ac5faee6"},"schema_version":"1.0","source":{"id":"2203.10315","kind":"arxiv","version":1}},"canonical_sha256":"8933ed7cc94ef6e4d56cbb5efd0f57016246c5b177a9810ce9ce447c0bfef8f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8933ed7cc94ef6e4d56cbb5efd0f57016246c5b177a9810ce9ce447c0bfef8f3","first_computed_at":"2026-07-05T04:06:51.573930Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:06:51.573930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CyPX2oO2odQVHobiuzMvUSYjd3iqpG3f1tZ4uRy4ScDYkQxLbL0319o/Sf37it7oOmYbKlPKhEgPDWmC+l71Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:06:51.574390Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.10315","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:86be76af5f98ee7016db969dcf1325e1be9e4fa2c1fa60c8740660fde0c73467","sha256:ba3c015af77665357f447f2deab5d140cd9f0ad3924a22f3e709331910675c8f"],"state_sha256":"0a60f4359ee2604bd21f76f7b76f9030ec7d890e95dbff4ada1f62f43f39c0a8"}