{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RQA7EXQSPDKPIVDRCY5EQ5CNDH","short_pith_number":"pith:RQA7EXQS","schema_version":"1.0","canonical_sha256":"8c01f25e1278d4f45471163a48744d19c4fffe596f67aaffcfb4b09f96f0d7b8","source":{"kind":"arxiv","id":"1709.03815","version":1},"attestation_state":"computed","paper":{"title":"OpenNMT: Open-source Toolkit for Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander M. Rush, Guillaume Klein, Jean Senellart, Josep Crego, Yoon Kim, Yuntian Deng","submitted_at":"2017-09-12T12:58:07Z","abstract_excerpt":"We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintaining competitive performance, modularity and reasonable training requirements."},"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":"1709.03815","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-12T12:58:07Z","cross_cats_sorted":[],"title_canon_sha256":"3ae047385eb5e7a84ec3ed5f6464f7834cbaf4ce532584b16e8f1af57bd69508","abstract_canon_sha256":"0e58d2b2fec54177c2e0b9dcb3a210c8b68e1d30d71e0c6d12cee7fa12c78942"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:29.358462Z","signature_b64":"y2VRDsyXyu7ihGBc3JYWJDGyS7n2Y90/4dNzvC3tetixd8KLuWMWGf5j7TnVEbhATsRNuISUklzV5rUFmSpWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c01f25e1278d4f45471163a48744d19c4fffe596f67aaffcfb4b09f96f0d7b8","last_reissued_at":"2026-05-18T00:35:29.357656Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:29.357656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenNMT: Open-source Toolkit for Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander M. Rush, Guillaume Klein, Jean Senellart, Josep Crego, Yoon Kim, Yuntian Deng","submitted_at":"2017-09-12T12:58:07Z","abstract_excerpt":"We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintaining competitive performance, modularity and reasonable training requirements."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.03815","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":"1709.03815","created_at":"2026-05-18T00:35:29.357790+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.03815v1","created_at":"2026-05-18T00:35:29.357790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.03815","created_at":"2026-05-18T00:35:29.357790+00:00"},{"alias_kind":"pith_short_12","alias_value":"RQA7EXQSPDKP","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"RQA7EXQSPDKPIVDR","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"RQA7EXQS","created_at":"2026-05-18T12:31:39.905425+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/RQA7EXQSPDKPIVDRCY5EQ5CNDH","json":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH.json","graph_json":"https://pith.science/api/pith-number/RQA7EXQSPDKPIVDRCY5EQ5CNDH/graph.json","events_json":"https://pith.science/api/pith-number/RQA7EXQSPDKPIVDRCY5EQ5CNDH/events.json","paper":"https://pith.science/paper/RQA7EXQS"},"agent_actions":{"view_html":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH","download_json":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH.json","view_paper":"https://pith.science/paper/RQA7EXQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.03815&json=true","fetch_graph":"https://pith.science/api/pith-number/RQA7EXQSPDKPIVDRCY5EQ5CNDH/graph.json","fetch_events":"https://pith.science/api/pith-number/RQA7EXQSPDKPIVDRCY5EQ5CNDH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH/action/storage_attestation","attest_author":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH/action/author_attestation","sign_citation":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH/action/citation_signature","submit_replication":"https://pith.science/pith/RQA7EXQSPDKPIVDRCY5EQ5CNDH/action/replication_record"}},"created_at":"2026-05-18T00:35:29.357790+00:00","updated_at":"2026-05-18T00:35:29.357790+00:00"}