{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AWYAJIKSZITLFEZFDAZFI5ZSLC","short_pith_number":"pith:AWYAJIKS","schema_version":"1.0","canonical_sha256":"05b004a152ca26b29325183254773258b5abb598f42a81b79d6d32fc34fb190c","source":{"kind":"arxiv","id":"1812.04898","version":1},"attestation_state":"computed","paper":{"title":"SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dipankar Das, Sainik Kumar Mahata, Sivaji Bandyopadhyay, Soumil Mandal","submitted_at":"2018-12-12T11:11:08Z","abstract_excerpt":"In the present article, we identified the qualitative differences between Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) outputs. We have tried to answer two important questions: 1. Does NMT perform equivalently well with respect to SMT and 2. Does it add extra flavor in improving the quality of MT output by employing simple sentences as training units. In order to obtain insights, we have developed three core models viz., SMT model based on Moses toolkit, followed by character and word level NMT models. All of the systems use English-Hindi and English-Bengali langu"},"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":"1812.04898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-12T11:11:08Z","cross_cats_sorted":[],"title_canon_sha256":"29b8e3b389f2b5c2b8be11a269d47b6f702339d31828efc5310cf0148a41eefe","abstract_canon_sha256":"e7670097f7f082f0727993f2ef81bd5352d3add70454ef9d62461fed50499e57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:26.998935Z","signature_b64":"fpeRLvJn9n/bDQW0jVvUuq8IjBXKSq9/UlKzXWiKb8767WeS5ZiYxY6fwKIzF7ZYii6qUhYChXhGJ1yhe87JAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05b004a152ca26b29325183254773258b5abb598f42a81b79d6d32fc34fb190c","last_reissued_at":"2026-05-17T23:58:26.998539Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:26.998539Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dipankar Das, Sainik Kumar Mahata, Sivaji Bandyopadhyay, Soumil Mandal","submitted_at":"2018-12-12T11:11:08Z","abstract_excerpt":"In the present article, we identified the qualitative differences between Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) outputs. We have tried to answer two important questions: 1. Does NMT perform equivalently well with respect to SMT and 2. Does it add extra flavor in improving the quality of MT output by employing simple sentences as training units. In order to obtain insights, we have developed three core models viz., SMT model based on Moses toolkit, followed by character and word level NMT models. All of the systems use English-Hindi and English-Bengali langu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04898","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":"1812.04898","created_at":"2026-05-17T23:58:26.998596+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.04898v1","created_at":"2026-05-17T23:58:26.998596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04898","created_at":"2026-05-17T23:58:26.998596+00:00"},{"alias_kind":"pith_short_12","alias_value":"AWYAJIKSZITL","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AWYAJIKSZITLFEZF","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AWYAJIKS","created_at":"2026-05-18T12:32:13.499390+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/AWYAJIKSZITLFEZFDAZFI5ZSLC","json":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC.json","graph_json":"https://pith.science/api/pith-number/AWYAJIKSZITLFEZFDAZFI5ZSLC/graph.json","events_json":"https://pith.science/api/pith-number/AWYAJIKSZITLFEZFDAZFI5ZSLC/events.json","paper":"https://pith.science/paper/AWYAJIKS"},"agent_actions":{"view_html":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC","download_json":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC.json","view_paper":"https://pith.science/paper/AWYAJIKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.04898&json=true","fetch_graph":"https://pith.science/api/pith-number/AWYAJIKSZITLFEZFDAZFI5ZSLC/graph.json","fetch_events":"https://pith.science/api/pith-number/AWYAJIKSZITLFEZFDAZFI5ZSLC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC/action/storage_attestation","attest_author":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC/action/author_attestation","sign_citation":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC/action/citation_signature","submit_replication":"https://pith.science/pith/AWYAJIKSZITLFEZFDAZFI5ZSLC/action/replication_record"}},"created_at":"2026-05-17T23:58:26.998596+00:00","updated_at":"2026-05-17T23:58:26.998596+00:00"}