{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RYD2GGMJUADW4J6MS7LOVXRVKK","short_pith_number":"pith:RYD2GGMJ","canonical_record":{"source":{"id":"2210.10305","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-19T05:33:27Z","cross_cats_sorted":[],"title_canon_sha256":"d6276afb1325e222d2f70d2afc6ffc1dc55f2b5a95dbd6d7d6e6f8ab15bae54b","abstract_canon_sha256":"81cf5bf6d7edff1e860e8c0ff8aab740ef10203fe296a8a121304fe602a15725"},"schema_version":"1.0"},"canonical_sha256":"8e07a31989a0076e27cc97d6eade35528e53846225201f7e08ccf445ba901ddd","source":{"kind":"arxiv","id":"2210.10305","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.10305","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"arxiv_version","alias_value":"2210.10305v2","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.10305","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"pith_short_12","alias_value":"RYD2GGMJUADW","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"pith_short_16","alias_value":"RYD2GGMJUADW4J6M","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"pith_short_8","alias_value":"RYD2GGMJ","created_at":"2026-07-05T05:48:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RYD2GGMJUADW4J6MS7LOVXRVKK","target":"record","payload":{"canonical_record":{"source":{"id":"2210.10305","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-19T05:33:27Z","cross_cats_sorted":[],"title_canon_sha256":"d6276afb1325e222d2f70d2afc6ffc1dc55f2b5a95dbd6d7d6e6f8ab15bae54b","abstract_canon_sha256":"81cf5bf6d7edff1e860e8c0ff8aab740ef10203fe296a8a121304fe602a15725"},"schema_version":"1.0"},"canonical_sha256":"8e07a31989a0076e27cc97d6eade35528e53846225201f7e08ccf445ba901ddd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:48:02.452607Z","signature_b64":"6AiXj1lW4hY86A6jpws1PBmCKxOMF/XNwLb4xV7/Av3GNopyrsmxXRiU97ciqvD+9uEZk/nOUzTdLtVeGeZVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e07a31989a0076e27cc97d6eade35528e53846225201f7e08ccf445ba901ddd","last_reissued_at":"2026-07-05T05:48:02.452161Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:48:02.452161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.10305","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":"2026-07-05T05:48:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6a1ObHyeGQe8ot/1+wBy44CYYpOZOL6k/OV53pDJWfie1fzgHs2EPoUt6Clt2O9cKP3AhFO9Q2VyxCSQHC41Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:28:56.643910Z"},"content_sha256":"ec5fd368686f82cc655ff0b2afc8cc82366a58a711f28a30afbbb0964c3b40bb","schema_version":"1.0","event_id":"sha256:ec5fd368686f82cc655ff0b2afc8cc82366a58a711f28a30afbbb0964c3b40bb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RYD2GGMJUADW4J6MS7LOVXRVKK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Wenbiao Li, Yunfang Wu, Ziyang Wang","submitted_at":"2022-10-19T05:33:27Z","abstract_excerpt":"For readability assessment, traditional methods mainly employ machine learning classifiers with hundreds of linguistic features. Although the deep learning model has become the prominent approach for almost all NLP tasks, it is less explored for readability assessment. In this paper, we propose a BERT-based model with feature projection and length-balanced loss (BERT-FP-LBL) for readability assessment. Specially, we present a new difficulty knowledge guided semi-supervised method to extract topic features to complement the traditional linguistic features. From the linguistic features, we emplo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.10305","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/2210.10305/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":"2026-07-05T05:48:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ya5SoGnoKYR95Ho+LctucGMDaejNkSxdNigc2L1naHCSKP4Vv63T0vVTS22KU8FoOQG5b4WWcOP4QxKRy6tHCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:28:56.644943Z"},"content_sha256":"6a5bc811ff5454eb17b9bd3ef37e933b344fe477969c8e55da36250893ac0a23","schema_version":"1.0","event_id":"sha256:6a5bc811ff5454eb17b9bd3ef37e933b344fe477969c8e55da36250893ac0a23"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RYD2GGMJUADW4J6MS7LOVXRVKK/bundle.json","state_url":"https://pith.science/pith/RYD2GGMJUADW4J6MS7LOVXRVKK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RYD2GGMJUADW4J6MS7LOVXRVKK/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":"2026-08-16T18:28:56Z","links":{"resolver":"https://pith.science/pith/RYD2GGMJUADW4J6MS7LOVXRVKK","bundle":"https://pith.science/pith/RYD2GGMJUADW4J6MS7LOVXRVKK/bundle.json","state":"https://pith.science/pith/RYD2GGMJUADW4J6MS7LOVXRVKK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RYD2GGMJUADW4J6MS7LOVXRVKK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RYD2GGMJUADW4J6MS7LOVXRVKK","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":"81cf5bf6d7edff1e860e8c0ff8aab740ef10203fe296a8a121304fe602a15725","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-19T05:33:27Z","title_canon_sha256":"d6276afb1325e222d2f70d2afc6ffc1dc55f2b5a95dbd6d7d6e6f8ab15bae54b"},"schema_version":"1.0","source":{"id":"2210.10305","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.10305","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"arxiv_version","alias_value":"2210.10305v2","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.10305","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"pith_short_12","alias_value":"RYD2GGMJUADW","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"pith_short_16","alias_value":"RYD2GGMJUADW4J6M","created_at":"2026-07-05T05:48:02Z"},{"alias_kind":"pith_short_8","alias_value":"RYD2GGMJ","created_at":"2026-07-05T05:48:02Z"}],"graph_snapshots":[{"event_id":"sha256:6a5bc811ff5454eb17b9bd3ef37e933b344fe477969c8e55da36250893ac0a23","target":"graph","created_at":"2026-07-05T05:48:02Z","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/2210.10305/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"For readability assessment, traditional methods mainly employ machine learning classifiers with hundreds of linguistic features. Although the deep learning model has become the prominent approach for almost all NLP tasks, it is less explored for readability assessment. In this paper, we propose a BERT-based model with feature projection and length-balanced loss (BERT-FP-LBL) for readability assessment. Specially, we present a new difficulty knowledge guided semi-supervised method to extract topic features to complement the traditional linguistic features. From the linguistic features, we emplo","authors_text":"Wenbiao Li, Yunfang Wu, Ziyang Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-19T05:33:27Z","title":"A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.10305","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:ec5fd368686f82cc655ff0b2afc8cc82366a58a711f28a30afbbb0964c3b40bb","target":"record","created_at":"2026-07-05T05:48:02Z","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":"81cf5bf6d7edff1e860e8c0ff8aab740ef10203fe296a8a121304fe602a15725","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-19T05:33:27Z","title_canon_sha256":"d6276afb1325e222d2f70d2afc6ffc1dc55f2b5a95dbd6d7d6e6f8ab15bae54b"},"schema_version":"1.0","source":{"id":"2210.10305","kind":"arxiv","version":2}},"canonical_sha256":"8e07a31989a0076e27cc97d6eade35528e53846225201f7e08ccf445ba901ddd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e07a31989a0076e27cc97d6eade35528e53846225201f7e08ccf445ba901ddd","first_computed_at":"2026-07-05T05:48:02.452161Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:48:02.452161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6AiXj1lW4hY86A6jpws1PBmCKxOMF/XNwLb4xV7/Av3GNopyrsmxXRiU97ciqvD+9uEZk/nOUzTdLtVeGeZVAA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:48:02.452607Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.10305","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec5fd368686f82cc655ff0b2afc8cc82366a58a711f28a30afbbb0964c3b40bb","sha256:6a5bc811ff5454eb17b9bd3ef37e933b344fe477969c8e55da36250893ac0a23"],"state_sha256":"873c28d3e4ab3bab0f93a8b4b491adf0dfbf98aece2ef41dda0138a9867fac66"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vXj2SErobcEZh85eG5rveCBe0Hto618RCACm6dwVbjB+NHiE6bcAaou/OtRIpH80WpdHaTSO1SgfA8rSC7WRAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T18:28:56.650295Z","bundle_sha256":"e6c4040fb373e18b5522991e62cb7da3796c68c37e0e01dd0658d4481b06b366"}}