{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XYA7MC5XDMULYHFQSTK5YI44YN","short_pith_number":"pith:XYA7MC5X","schema_version":"1.0","canonical_sha256":"be01f60bb71b28bc1cb094d5dc239cc35eec1539ae6d55f924353827880b6d95","source":{"kind":"arxiv","id":"1811.02510","version":1},"attestation_state":"computed","paper":{"title":"UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Felipe S\\'anchez-Mart\\'inez, Mikel L. Forcada, Miquel Espl\\`a-Gomis","submitted_at":"2018-11-06T17:25:21Z","abstract_excerpt":"We describe the Universitat d'Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018. Our approach to word-level MT QE builds on previous work to mark the words in the machine-translated sentence as \\textit{OK} or \\textit{BAD}, and is extended to determine if a word or sequence of words need to be inserted in the gap after each word. Our sentence-level submission simply uses the edit operations predicted by the word-level approach to approximate TER. The method presented ranked first in the sub-task of identifying insertions"},"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":"1811.02510","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-06T17:25:21Z","cross_cats_sorted":[],"title_canon_sha256":"979528944c83dda6290e02ba8cc0cd0afd810dcf96f96c77029d6796e1c57e3f","abstract_canon_sha256":"90a807f28859a74bdbc4485f0c2b6ebed386ca520c1a41b8816807f96e23d195"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:24.065599Z","signature_b64":"5NJ3qSqKIxx7dcIm6h7C9JhhsL8KxMROHb3YFkYDw3wfJjX+PoRZBphx0D/j4uxzg1jihQdycDAU0yXy5/u8Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be01f60bb71b28bc1cb094d5dc239cc35eec1539ae6d55f924353827880b6d95","last_reissued_at":"2026-05-18T00:01:24.065175Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:24.065175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Felipe S\\'anchez-Mart\\'inez, Mikel L. Forcada, Miquel Espl\\`a-Gomis","submitted_at":"2018-11-06T17:25:21Z","abstract_excerpt":"We describe the Universitat d'Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018. Our approach to word-level MT QE builds on previous work to mark the words in the machine-translated sentence as \\textit{OK} or \\textit{BAD}, and is extended to determine if a word or sequence of words need to be inserted in the gap after each word. Our sentence-level submission simply uses the edit operations predicted by the word-level approach to approximate TER. The method presented ranked first in the sub-task of identifying insertions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02510","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":""},"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":"1811.02510","created_at":"2026-05-18T00:01:24.065232+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.02510v1","created_at":"2026-05-18T00:01:24.065232+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02510","created_at":"2026-05-18T00:01:24.065232+00:00"},{"alias_kind":"pith_short_12","alias_value":"XYA7MC5XDMUL","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"XYA7MC5XDMULYHFQ","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"XYA7MC5X","created_at":"2026-05-18T12:33:04.347982+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/XYA7MC5XDMULYHFQSTK5YI44YN","json":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN.json","graph_json":"https://pith.science/api/pith-number/XYA7MC5XDMULYHFQSTK5YI44YN/graph.json","events_json":"https://pith.science/api/pith-number/XYA7MC5XDMULYHFQSTK5YI44YN/events.json","paper":"https://pith.science/paper/XYA7MC5X"},"agent_actions":{"view_html":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN","download_json":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN.json","view_paper":"https://pith.science/paper/XYA7MC5X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.02510&json=true","fetch_graph":"https://pith.science/api/pith-number/XYA7MC5XDMULYHFQSTK5YI44YN/graph.json","fetch_events":"https://pith.science/api/pith-number/XYA7MC5XDMULYHFQSTK5YI44YN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN/action/storage_attestation","attest_author":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN/action/author_attestation","sign_citation":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN/action/citation_signature","submit_replication":"https://pith.science/pith/XYA7MC5XDMULYHFQSTK5YI44YN/action/replication_record"}},"created_at":"2026-05-18T00:01:24.065232+00:00","updated_at":"2026-05-18T00:01:24.065232+00:00"}