{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:67DHYZFE4Q4HMUHUOZ2O2RR5EF","short_pith_number":"pith:67DHYZFE","schema_version":"1.0","canonical_sha256":"f7c67c64a4e4387650f47674ed463d216e58d2955d18cc734c3ba0bb94086e6e","source":{"kind":"arxiv","id":"2004.07737","version":2},"attestation_state":"computed","paper":{"title":"Cross-lingual Contextualized Topic Models with Zero-shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Debora Nozza, Dirk Hovy, Elisabetta Fersini, Federico Bianchi, Silvia Terragni","submitted_at":"2020-04-16T16:21:17Z","abstract_excerpt":"Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models. Models have to be either single-language or suffer from a huge, but extremely sparse vocabulary. Both issues can be addressed by transfer learning. In this paper, we introduce a zero-shot cross-lingual topic model. Our model learns topics on one language (here, English), and predicts them for unseen documents in different languages (here, Italian, French, German, and Port"},"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":"2004.07737","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-04-16T16:21:17Z","cross_cats_sorted":[],"title_canon_sha256":"c65d74f24d6f5ee26a58e33f27b20d970201b2e7a5323be3e585af3919fa5e01","abstract_canon_sha256":"e58fa82023f00ea1eb37cfbc729a31329a88292dc7e8f93afb98dc6e08152045"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:12:41.130445Z","signature_b64":"h+2Z8hx7ShTdGrOC1Ob17zMmFrDu0hrNaYGRggOz6QbIrXeWug37AXv5t1JQEt5RvrPWzwk4cszaworhGtqtCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7c67c64a4e4387650f47674ed463d216e58d2955d18cc734c3ba0bb94086e6e","last_reissued_at":"2026-07-05T02:12:41.129955Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:12:41.129955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-lingual Contextualized Topic Models with Zero-shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Debora Nozza, Dirk Hovy, Elisabetta Fersini, Federico Bianchi, Silvia Terragni","submitted_at":"2020-04-16T16:21:17Z","abstract_excerpt":"Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models. Models have to be either single-language or suffer from a huge, but extremely sparse vocabulary. Both issues can be addressed by transfer learning. In this paper, we introduce a zero-shot cross-lingual topic model. Our model learns topics on one language (here, English), and predicts them for unseen documents in different languages (here, Italian, French, German, and Port"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.07737","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/2004.07737/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":"2004.07737","created_at":"2026-07-05T02:12:41.130013+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.07737v2","created_at":"2026-07-05T02:12:41.130013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.07737","created_at":"2026-07-05T02:12:41.130013+00:00"},{"alias_kind":"pith_short_12","alias_value":"67DHYZFE4Q4H","created_at":"2026-07-05T02:12:41.130013+00:00"},{"alias_kind":"pith_short_16","alias_value":"67DHYZFE4Q4HMUHU","created_at":"2026-07-05T02:12:41.130013+00:00"},{"alias_kind":"pith_short_8","alias_value":"67DHYZFE","created_at":"2026-07-05T02:12:41.130013+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.04643","citing_title":"Graph-Augmented LLMs for Swiss MP Ideology Prediction","ref_index":193,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF","json":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF.json","graph_json":"https://pith.science/api/pith-number/67DHYZFE4Q4HMUHUOZ2O2RR5EF/graph.json","events_json":"https://pith.science/api/pith-number/67DHYZFE4Q4HMUHUOZ2O2RR5EF/events.json","paper":"https://pith.science/paper/67DHYZFE"},"agent_actions":{"view_html":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF","download_json":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF.json","view_paper":"https://pith.science/paper/67DHYZFE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.07737&json=true","fetch_graph":"https://pith.science/api/pith-number/67DHYZFE4Q4HMUHUOZ2O2RR5EF/graph.json","fetch_events":"https://pith.science/api/pith-number/67DHYZFE4Q4HMUHUOZ2O2RR5EF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF/action/storage_attestation","attest_author":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF/action/author_attestation","sign_citation":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF/action/citation_signature","submit_replication":"https://pith.science/pith/67DHYZFE4Q4HMUHUOZ2O2RR5EF/action/replication_record"}},"created_at":"2026-07-05T02:12:41.130013+00:00","updated_at":"2026-07-05T02:12:41.130013+00:00"}