{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CDA37TCRPSHSUS6GCSMSSV3OML","short_pith_number":"pith:CDA37TCR","schema_version":"1.0","canonical_sha256":"10c1bfcc517c8f2a4bc6149929576e62c2c40e94f848b32ece321cbe190557b5","source":{"kind":"arxiv","id":"1909.09962","version":3},"attestation_state":"computed","paper":{"title":"Adapting Language Models for Non-Parallel Author-Stylized Rewriting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Anandhavelu Natarajan, Bakhtiyar Syed, Balaji Vasan Srinivasan, Gaurav Verma, Vasudeva Varma","submitted_at":"2019-09-22T08:13:28Z","abstract_excerpt":"Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author's style. Our proposed approach adapts a pre-trained language model to generate author-stylized text by fine-tuning on the author-specific corpus using a denoising autoencoder (DAE) loss in a cascaded encoder-decoder framework. Optimizing over DAE loss allows our model to learn the nuances of an author's style without relying on pa"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.09962","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-22T08:13:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9aa3e2e030792a3c04324207d978c9c5e5ede428cb0e78a6561d428b998da6a3","abstract_canon_sha256":"75950e3d4f72573e0202b195e0c138696b4c5ccea2e853f42c308bf82cdbfa0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:47:54.296752Z","signature_b64":"+1dXQmA5QhZmXBwbU+od5n9XjBwc2FP+W9oGGU50KHGItUwdrayBUYfzOrV1scS0V7rkfdQDDIxbhaWqN/A6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10c1bfcc517c8f2a4bc6149929576e62c2c40e94f848b32ece321cbe190557b5","last_reissued_at":"2026-07-05T01:47:54.296381Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:47:54.296381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adapting Language Models for Non-Parallel Author-Stylized Rewriting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Anandhavelu Natarajan, Bakhtiyar Syed, Balaji Vasan Srinivasan, Gaurav Verma, Vasudeva Varma","submitted_at":"2019-09-22T08:13:28Z","abstract_excerpt":"Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author's style. Our proposed approach adapts a pre-trained language model to generate author-stylized text by fine-tuning on the author-specific corpus using a denoising autoencoder (DAE) loss in a cascaded encoder-decoder framework. Optimizing over DAE loss allows our model to learn the nuances of an author's style without relying on pa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.09962","kind":"arxiv","version":3},"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/1909.09962/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.09962","created_at":"2026-07-05T01:47:54.296438+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.09962v3","created_at":"2026-07-05T01:47:54.296438+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.09962","created_at":"2026-07-05T01:47:54.296438+00:00"},{"alias_kind":"pith_short_12","alias_value":"CDA37TCRPSHS","created_at":"2026-07-05T01:47:54.296438+00:00"},{"alias_kind":"pith_short_16","alias_value":"CDA37TCRPSHSUS6G","created_at":"2026-07-05T01:47:54.296438+00:00"},{"alias_kind":"pith_short_8","alias_value":"CDA37TCR","created_at":"2026-07-05T01:47:54.296438+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML","json":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML.json","graph_json":"https://pith.science/api/pith-number/CDA37TCRPSHSUS6GCSMSSV3OML/graph.json","events_json":"https://pith.science/api/pith-number/CDA37TCRPSHSUS6GCSMSSV3OML/events.json","paper":"https://pith.science/paper/CDA37TCR"},"agent_actions":{"view_html":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML","download_json":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML.json","view_paper":"https://pith.science/paper/CDA37TCR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.09962&json=true","fetch_graph":"https://pith.science/api/pith-number/CDA37TCRPSHSUS6GCSMSSV3OML/graph.json","fetch_events":"https://pith.science/api/pith-number/CDA37TCRPSHSUS6GCSMSSV3OML/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML/action/storage_attestation","attest_author":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML/action/author_attestation","sign_citation":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML/action/citation_signature","submit_replication":"https://pith.science/pith/CDA37TCRPSHSUS6GCSMSSV3OML/action/replication_record"}},"created_at":"2026-07-05T01:47:54.296438+00:00","updated_at":"2026-07-05T01:47:54.296438+00:00"}