{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5WCNKYF3EHSHULHZJFET4XTDLA","short_pith_number":"pith:5WCNKYF3","schema_version":"1.0","canonical_sha256":"ed84d560bb21e47a2cf949493e5e6358316b39bd6bbcdd8a728069111d6c8f13","source":{"kind":"arxiv","id":"1805.03330","version":2},"attestation_state":"computed","paper":{"title":"Character-level Chinese-English Translation through ASCII Encoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mi Xue Tan, Nikola I. Nikolov, Richard H.R. Hahnloser, Yuhuang Hu","submitted_at":"2018-05-09T00:44:59Z","abstract_excerpt":"Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European language"},"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":"1805.03330","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-09T00:44:59Z","cross_cats_sorted":[],"title_canon_sha256":"26fce54f7decd97e3c7ffb99c22c8909b98f44345a5f6fbe95728c252da89104","abstract_canon_sha256":"2a2335438ec7840caf696e3547530e9d2215c4219cc044c0dc88622a71c330b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:17.619430Z","signature_b64":"kfju9ArE6yiAhymNw+SLwbyG6tz9Y9vO2tH9azV+mdyKUBvyhsnJ2TfCLhpVh3wy5bXWaIYj2eQdXkKLIZGVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed84d560bb21e47a2cf949493e5e6358316b39bd6bbcdd8a728069111d6c8f13","last_reissued_at":"2026-05-18T00:07:17.618785Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:17.618785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Character-level Chinese-English Translation through ASCII Encoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mi Xue Tan, Nikola I. Nikolov, Richard H.R. Hahnloser, Yuhuang Hu","submitted_at":"2018-05-09T00:44:59Z","abstract_excerpt":"Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European language"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.03330","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":""},"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":"1805.03330","created_at":"2026-05-18T00:07:17.618888+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.03330v2","created_at":"2026-05-18T00:07:17.618888+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.03330","created_at":"2026-05-18T00:07:17.618888+00:00"},{"alias_kind":"pith_short_12","alias_value":"5WCNKYF3EHSH","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5WCNKYF3EHSHULHZ","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5WCNKYF3","created_at":"2026-05-18T12:32:08.215937+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/5WCNKYF3EHSHULHZJFET4XTDLA","json":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA.json","graph_json":"https://pith.science/api/pith-number/5WCNKYF3EHSHULHZJFET4XTDLA/graph.json","events_json":"https://pith.science/api/pith-number/5WCNKYF3EHSHULHZJFET4XTDLA/events.json","paper":"https://pith.science/paper/5WCNKYF3"},"agent_actions":{"view_html":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA","download_json":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA.json","view_paper":"https://pith.science/paper/5WCNKYF3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.03330&json=true","fetch_graph":"https://pith.science/api/pith-number/5WCNKYF3EHSHULHZJFET4XTDLA/graph.json","fetch_events":"https://pith.science/api/pith-number/5WCNKYF3EHSHULHZJFET4XTDLA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA/action/storage_attestation","attest_author":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA/action/author_attestation","sign_citation":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA/action/citation_signature","submit_replication":"https://pith.science/pith/5WCNKYF3EHSHULHZJFET4XTDLA/action/replication_record"}},"created_at":"2026-05-18T00:07:17.618888+00:00","updated_at":"2026-05-18T00:07:17.618888+00:00"}