{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:IIG5BNKJ3ASOBKQHBALAHAHXT2","short_pith_number":"pith:IIG5BNKJ","schema_version":"1.0","canonical_sha256":"420dd0b549d824e0aa0708160380f79e9fbc095ef3bb1c660f285bbe161d72f5","source":{"kind":"arxiv","id":"1610.07272","version":1},"attestation_state":"computed","paper":{"title":"Bridging Neural Machine Translation and Bilingual Dictionaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Jiajun Zhang","submitted_at":"2016-10-24T03:39:22Z","abstract_excerpt":"Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/chara"},"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":"1610.07272","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-10-24T03:39:22Z","cross_cats_sorted":[],"title_canon_sha256":"fdb95729c2adc28e3230c9b385fddda32e39f7ab59a63dd96197884d07c37155","abstract_canon_sha256":"2001c999693794ab2989339a4bbdf2d779e99debf92e50432b7fe2c4dd9640f6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:30.516883Z","signature_b64":"sBC2bajZcV17LgHh283G6YKLYBxxtxCvD6m9ikz2DXicSgs+5MjmQnjqlrYoFGjNm6E1lTZ8B7zbZ95ecwHpAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"420dd0b549d824e0aa0708160380f79e9fbc095ef3bb1c660f285bbe161d72f5","last_reissued_at":"2026-05-18T01:01:30.516426Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:30.516426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging Neural Machine Translation and Bilingual Dictionaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Jiajun Zhang","submitted_at":"2016-10-24T03:39:22Z","abstract_excerpt":"Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/chara"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.07272","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":"1610.07272","created_at":"2026-05-18T01:01:30.516485+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.07272v1","created_at":"2026-05-18T01:01:30.516485+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.07272","created_at":"2026-05-18T01:01:30.516485+00:00"},{"alias_kind":"pith_short_12","alias_value":"IIG5BNKJ3ASO","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"IIG5BNKJ3ASOBKQH","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"IIG5BNKJ","created_at":"2026-05-18T12:30:22.444734+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2411.01141","citing_title":"Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2507.18902","citing_title":"SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2","json":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2.json","graph_json":"https://pith.science/api/pith-number/IIG5BNKJ3ASOBKQHBALAHAHXT2/graph.json","events_json":"https://pith.science/api/pith-number/IIG5BNKJ3ASOBKQHBALAHAHXT2/events.json","paper":"https://pith.science/paper/IIG5BNKJ"},"agent_actions":{"view_html":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2","download_json":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2.json","view_paper":"https://pith.science/paper/IIG5BNKJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.07272&json=true","fetch_graph":"https://pith.science/api/pith-number/IIG5BNKJ3ASOBKQHBALAHAHXT2/graph.json","fetch_events":"https://pith.science/api/pith-number/IIG5BNKJ3ASOBKQHBALAHAHXT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2/action/storage_attestation","attest_author":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2/action/author_attestation","sign_citation":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2/action/citation_signature","submit_replication":"https://pith.science/pith/IIG5BNKJ3ASOBKQHBALAHAHXT2/action/replication_record"}},"created_at":"2026-05-18T01:01:30.516485+00:00","updated_at":"2026-05-18T01:01:30.516485+00:00"}