{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XJXEMNTCT5SIIHSTLWLLBTWXUT","short_pith_number":"pith:XJXEMNTC","schema_version":"1.0","canonical_sha256":"ba6e4636629f64841e535d96b0ced7a4e1d279eaf8bc41a273921f237544d176","source":{"kind":"arxiv","id":"2107.07958","version":1},"attestation_state":"computed","paper":{"title":"Temporal-aware Language Representation Learning From Crowdsourced Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Wenbiao Ding, Xiao Zhai, Yang Hao, Zitao Liu","submitted_at":"2021-07-15T05:25:56Z","abstract_excerpt":"Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose \\emph{TACMA}, a \\underline{t}emporal-\\underline{a}ware language representation learning heurist"},"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":"2107.07958","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-15T05:25:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0ddcf6dab9692a18fe52da0c465a78be579611e9075cbc9afb4ea96b9768be10","abstract_canon_sha256":"14767149389d5343144175a76cc7d29d0714b94d4c26c7380dbd7caf44ed74c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:58:31.124891Z","signature_b64":"np+aOK7GXcpSHc47N/O1LlOvFYGMJJgWIrJAakarW30ffDNaOsbVGnb3lcs0a94dyp73/n/vfcfNaHzWEpxUBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba6e4636629f64841e535d96b0ced7a4e1d279eaf8bc41a273921f237544d176","last_reissued_at":"2026-07-05T02:58:31.124475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:58:31.124475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Temporal-aware Language Representation Learning From Crowdsourced Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Wenbiao Ding, Xiao Zhai, Yang Hao, Zitao Liu","submitted_at":"2021-07-15T05:25:56Z","abstract_excerpt":"Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose \\emph{TACMA}, a \\underline{t}emporal-\\underline{a}ware language representation learning heurist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.07958","kind":"arxiv","version":1},"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/2107.07958/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":"2107.07958","created_at":"2026-07-05T02:58:31.124537+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.07958v1","created_at":"2026-07-05T02:58:31.124537+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.07958","created_at":"2026-07-05T02:58:31.124537+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJXEMNTCT5SI","created_at":"2026-07-05T02:58:31.124537+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJXEMNTCT5SIIHST","created_at":"2026-07-05T02:58:31.124537+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJXEMNTC","created_at":"2026-07-05T02:58:31.124537+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/XJXEMNTCT5SIIHSTLWLLBTWXUT","json":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT.json","graph_json":"https://pith.science/api/pith-number/XJXEMNTCT5SIIHSTLWLLBTWXUT/graph.json","events_json":"https://pith.science/api/pith-number/XJXEMNTCT5SIIHSTLWLLBTWXUT/events.json","paper":"https://pith.science/paper/XJXEMNTC"},"agent_actions":{"view_html":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT","download_json":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT.json","view_paper":"https://pith.science/paper/XJXEMNTC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.07958&json=true","fetch_graph":"https://pith.science/api/pith-number/XJXEMNTCT5SIIHSTLWLLBTWXUT/graph.json","fetch_events":"https://pith.science/api/pith-number/XJXEMNTCT5SIIHSTLWLLBTWXUT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT/action/storage_attestation","attest_author":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT/action/author_attestation","sign_citation":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT/action/citation_signature","submit_replication":"https://pith.science/pith/XJXEMNTCT5SIIHSTLWLLBTWXUT/action/replication_record"}},"created_at":"2026-07-05T02:58:31.124537+00:00","updated_at":"2026-07-05T02:58:31.124537+00:00"}