{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:O4XZPPJ76YULFRIK4IMAB5RDCT","short_pith_number":"pith:O4XZPPJ7","schema_version":"1.0","canonical_sha256":"772f97bd3ff628b2c50ae21800f62314d27fc38f1f58b50e85797386768f0ba5","source":{"kind":"arxiv","id":"2205.09178","version":2},"attestation_state":"computed","paper":{"title":"PreQuEL: Quality Estimation of Machine Translation Outputs in Advance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Leshem Choshen, Omri Abend, Shachar Don-Yehiya","submitted_at":"2022-05-18T18:55:05Z","abstract_excerpt":"We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A PreQuEL system predicts how well a given sentence will be translated, without recourse to the actual translation, thus eschewing unnecessary resource allocation when translation quality is bound to be low. PreQuEL can be defined relative to a given MT system (e.g., some industry service) or generally relative to the state-of-the-art. From a theoretical perspective, PreQuEL places the focus on the source text, tracing properties, possibly linguistic features, that make a sentence harder to machine translate.\n  We develop a bas"},"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":"2205.09178","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-18T18:55:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0f4453157b4fd4d20ba37cb0aa574cc304ce68cebd4682f59ace498a7fd7ad56","abstract_canon_sha256":"e0f08aef8900d2d172468cc98a7988b9b5787d2bff1b3f610d1efffdb7681d86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:22:07.974049Z","signature_b64":"iw0Fqvrb6NIbyW7jfI0lY7bnyY6U/bmyTmAvmxh7jZIoM4K3cn15Dw1Chhg5eag0LQTe5RG35uhFbZVe3GcsDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"772f97bd3ff628b2c50ae21800f62314d27fc38f1f58b50e85797386768f0ba5","last_reissued_at":"2026-07-05T05:22:07.973481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:22:07.973481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PreQuEL: Quality Estimation of Machine Translation Outputs in Advance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Leshem Choshen, Omri Abend, Shachar Don-Yehiya","submitted_at":"2022-05-18T18:55:05Z","abstract_excerpt":"We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A PreQuEL system predicts how well a given sentence will be translated, without recourse to the actual translation, thus eschewing unnecessary resource allocation when translation quality is bound to be low. PreQuEL can be defined relative to a given MT system (e.g., some industry service) or generally relative to the state-of-the-art. From a theoretical perspective, PreQuEL places the focus on the source text, tracing properties, possibly linguistic features, that make a sentence harder to machine translate.\n  We develop a bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09178","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/2205.09178/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":"2205.09178","created_at":"2026-07-05T05:22:07.973608+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.09178v2","created_at":"2026-07-05T05:22:07.973608+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09178","created_at":"2026-07-05T05:22:07.973608+00:00"},{"alias_kind":"pith_short_12","alias_value":"O4XZPPJ76YUL","created_at":"2026-07-05T05:22:07.973608+00:00"},{"alias_kind":"pith_short_16","alias_value":"O4XZPPJ76YULFRIK","created_at":"2026-07-05T05:22:07.973608+00:00"},{"alias_kind":"pith_short_8","alias_value":"O4XZPPJ7","created_at":"2026-07-05T05:22:07.973608+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/O4XZPPJ76YULFRIK4IMAB5RDCT","json":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT.json","graph_json":"https://pith.science/api/pith-number/O4XZPPJ76YULFRIK4IMAB5RDCT/graph.json","events_json":"https://pith.science/api/pith-number/O4XZPPJ76YULFRIK4IMAB5RDCT/events.json","paper":"https://pith.science/paper/O4XZPPJ7"},"agent_actions":{"view_html":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT","download_json":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT.json","view_paper":"https://pith.science/paper/O4XZPPJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.09178&json=true","fetch_graph":"https://pith.science/api/pith-number/O4XZPPJ76YULFRIK4IMAB5RDCT/graph.json","fetch_events":"https://pith.science/api/pith-number/O4XZPPJ76YULFRIK4IMAB5RDCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT/action/storage_attestation","attest_author":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT/action/author_attestation","sign_citation":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT/action/citation_signature","submit_replication":"https://pith.science/pith/O4XZPPJ76YULFRIK4IMAB5RDCT/action/replication_record"}},"created_at":"2026-07-05T05:22:07.973608+00:00","updated_at":"2026-07-05T05:22:07.973608+00:00"}