{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KRPI2UUVFCYHZMZZE7TOEVQLXH","short_pith_number":"pith:KRPI2UUV","schema_version":"1.0","canonical_sha256":"545e8d529528b07cb33927e6e2560bb9e93ab9f7241aed3c89f7175c1e407ba6","source":{"kind":"arxiv","id":"1906.05683","version":1},"attestation_state":"computed","paper":{"title":"Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jonathan May, Kevin Knight, Marjan Ghazvininejad, Nada Aldarrab, Nima Pourdamghani","submitted_at":"2019-06-11T17:56:29Z","abstract_excerpt":"Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then `translating' the resulting pseudo-translation, or `Translationese' into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting"},"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":"1906.05683","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-11T17:56:29Z","cross_cats_sorted":[],"title_canon_sha256":"2bddfc2ea0e359be35aaa5c7b7dbca62f1cd41dbf105e87bb60938be2498e74f","abstract_canon_sha256":"2a65ac408287bb72cc4da11ba116df95cd6835656c5ee68d213c0e19185a1698"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:24.610164Z","signature_b64":"REyR9S0qZVVeFbtfCBHWP3XarWSQw9ti5NEuzRI6qqPDO2guUzAEBqdXyR0iTzAgJb0uiYihB4TDZsiFus1ZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"545e8d529528b07cb33927e6e2560bb9e93ab9f7241aed3c89f7175c1e407ba6","last_reissued_at":"2026-05-17T23:43:24.609725Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:24.609725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jonathan May, Kevin Knight, Marjan Ghazvininejad, Nada Aldarrab, Nima Pourdamghani","submitted_at":"2019-06-11T17:56:29Z","abstract_excerpt":"Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then `translating' the resulting pseudo-translation, or `Translationese' into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05683","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":"1906.05683","created_at":"2026-05-17T23:43:24.609787+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.05683v1","created_at":"2026-05-17T23:43:24.609787+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05683","created_at":"2026-05-17T23:43:24.609787+00:00"},{"alias_kind":"pith_short_12","alias_value":"KRPI2UUVFCYH","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KRPI2UUVFCYHZMZZ","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KRPI2UUV","created_at":"2026-05-18T12:33:21.387695+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/KRPI2UUVFCYHZMZZE7TOEVQLXH","json":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH.json","graph_json":"https://pith.science/api/pith-number/KRPI2UUVFCYHZMZZE7TOEVQLXH/graph.json","events_json":"https://pith.science/api/pith-number/KRPI2UUVFCYHZMZZE7TOEVQLXH/events.json","paper":"https://pith.science/paper/KRPI2UUV"},"agent_actions":{"view_html":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH","download_json":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH.json","view_paper":"https://pith.science/paper/KRPI2UUV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.05683&json=true","fetch_graph":"https://pith.science/api/pith-number/KRPI2UUVFCYHZMZZE7TOEVQLXH/graph.json","fetch_events":"https://pith.science/api/pith-number/KRPI2UUVFCYHZMZZE7TOEVQLXH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH/action/storage_attestation","attest_author":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH/action/author_attestation","sign_citation":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH/action/citation_signature","submit_replication":"https://pith.science/pith/KRPI2UUVFCYHZMZZE7TOEVQLXH/action/replication_record"}},"created_at":"2026-05-17T23:43:24.609787+00:00","updated_at":"2026-05-17T23:43:24.609787+00:00"}