{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:H5JCPREDOHURE5OI7LFFDC7TRN","short_pith_number":"pith:H5JCPRED","schema_version":"1.0","canonical_sha256":"3f5227c48371e91275c8faca518bf38b56a3e8c940c61c63eb3ef639cb7feed6","source":{"kind":"arxiv","id":"2210.15696","version":1},"attestation_state":"computed","paper":{"title":"COMET-QE and Active Learning for Low-Resource Machine Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bruce A. Bassett, Everlyn Asiko Chimoto","submitted_at":"2022-10-27T18:00:41Z","abstract_excerpt":"Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish for our experiments, we show that COMET-QE significantly outperforms two variants of Round Trip Translation Likelihood (RTTL) and random sentence selection by up to 5 BLEU points for 20k sentences selected by Active Learning on a 30k baseline. This suggests that COMET-QE is a powerful tool for sentence selection in the very low-resource limit."},"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":"2210.15696","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-27T18:00:41Z","cross_cats_sorted":[],"title_canon_sha256":"0683955346f9f9d7070ce27367d0e5654a17e4271d1191b6a8af793e3e38a5f6","abstract_canon_sha256":"56b8517534066f5432f0724642c81525739d1680c4d4af6d875b1bab39d022bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:10.013447Z","signature_b64":"mUznMFyOvcrtEQmOJSL7C9hdQE00ZhmdC/PtF5MSUTsRlP1R/mF9qQOX1+fU6SegITVv3xB+sMYgqZOqDqZcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f5227c48371e91275c8faca518bf38b56a3e8c940c61c63eb3ef639cb7feed6","last_reissued_at":"2026-07-05T05:11:10.012946Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:10.012946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COMET-QE and Active Learning for Low-Resource Machine Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bruce A. Bassett, Everlyn Asiko Chimoto","submitted_at":"2022-10-27T18:00:41Z","abstract_excerpt":"Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish for our experiments, we show that COMET-QE significantly outperforms two variants of Round Trip Translation Likelihood (RTTL) and random sentence selection by up to 5 BLEU points for 20k sentences selected by Active Learning on a 30k baseline. This suggests that COMET-QE is a powerful tool for sentence selection in the very low-resource limit."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15696","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/2210.15696/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":"2210.15696","created_at":"2026-07-05T05:11:10.013007+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.15696v1","created_at":"2026-07-05T05:11:10.013007+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15696","created_at":"2026-07-05T05:11:10.013007+00:00"},{"alias_kind":"pith_short_12","alias_value":"H5JCPREDOHUR","created_at":"2026-07-05T05:11:10.013007+00:00"},{"alias_kind":"pith_short_16","alias_value":"H5JCPREDOHURE5OI","created_at":"2026-07-05T05:11:10.013007+00:00"},{"alias_kind":"pith_short_8","alias_value":"H5JCPRED","created_at":"2026-07-05T05:11:10.013007+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/H5JCPREDOHURE5OI7LFFDC7TRN","json":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN.json","graph_json":"https://pith.science/api/pith-number/H5JCPREDOHURE5OI7LFFDC7TRN/graph.json","events_json":"https://pith.science/api/pith-number/H5JCPREDOHURE5OI7LFFDC7TRN/events.json","paper":"https://pith.science/paper/H5JCPRED"},"agent_actions":{"view_html":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN","download_json":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN.json","view_paper":"https://pith.science/paper/H5JCPRED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.15696&json=true","fetch_graph":"https://pith.science/api/pith-number/H5JCPREDOHURE5OI7LFFDC7TRN/graph.json","fetch_events":"https://pith.science/api/pith-number/H5JCPREDOHURE5OI7LFFDC7TRN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN/action/storage_attestation","attest_author":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN/action/author_attestation","sign_citation":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN/action/citation_signature","submit_replication":"https://pith.science/pith/H5JCPREDOHURE5OI7LFFDC7TRN/action/replication_record"}},"created_at":"2026-07-05T05:11:10.013007+00:00","updated_at":"2026-07-05T05:11:10.013007+00:00"}