{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LYXBWIYDHTFPMWITGVOGADDBCN","short_pith_number":"pith:LYXBWIYD","schema_version":"1.0","canonical_sha256":"5e2e1b23033ccaf65913355c600c611369cf2b2f5aad6c7eea6cb3a7042a9f42","source":{"kind":"arxiv","id":"1904.09646","version":2},"attestation_state":"computed","paper":{"title":"Dynamic Past and Future for Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiajun Chen, Shujian Huang, Xin-Yu Dai, Zaixiang Zheng, Zhaopeng Tu","submitted_at":"2019-04-21T19:07:07Z","abstract_excerpt":"Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated (Past) and untranslated (Future) to groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (Sabour et al., 2017), namely {\\em Guided Dynamic Routing}, where the translating status at each decoding step {\\em guides} the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling tra"},"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":"1904.09646","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-21T19:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"e0300980deff912b30a80aaf941305c58a7dfaa0c76ca03a765540072976595a","abstract_canon_sha256":"69f527c52285d24025e09cd7c402a610811f277a831602de77775c5f031fcfe5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:05:53.419732Z","signature_b64":"aezbS3BDrvnVsT6IExiFzp/xM702hZCCfQyf8Oh59jwxT/vrq0hqEX6PcEeOFpMWiADoAfybsdGNszKYGA7GCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e2e1b23033ccaf65913355c600c611369cf2b2f5aad6c7eea6cb3a7042a9f42","last_reissued_at":"2026-07-05T00:05:53.419248Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:05:53.419248Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Past and Future for Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiajun Chen, Shujian Huang, Xin-Yu Dai, Zaixiang Zheng, Zhaopeng Tu","submitted_at":"2019-04-21T19:07:07Z","abstract_excerpt":"Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated (Past) and untranslated (Future) to groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (Sabour et al., 2017), namely {\\em Guided Dynamic Routing}, where the translating status at each decoding step {\\em guides} the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09646","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1904.09646/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1904.09646","created_at":"2026-07-05T00:05:53.419304+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.09646v2","created_at":"2026-07-05T00:05:53.419304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09646","created_at":"2026-07-05T00:05:53.419304+00:00"},{"alias_kind":"pith_short_12","alias_value":"LYXBWIYDHTFP","created_at":"2026-07-05T00:05:53.419304+00:00"},{"alias_kind":"pith_short_16","alias_value":"LYXBWIYDHTFPMWIT","created_at":"2026-07-05T00:05:53.419304+00:00"},{"alias_kind":"pith_short_8","alias_value":"LYXBWIYD","created_at":"2026-07-05T00:05:53.419304+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/LYXBWIYDHTFPMWITGVOGADDBCN","json":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN.json","graph_json":"https://pith.science/api/pith-number/LYXBWIYDHTFPMWITGVOGADDBCN/graph.json","events_json":"https://pith.science/api/pith-number/LYXBWIYDHTFPMWITGVOGADDBCN/events.json","paper":"https://pith.science/paper/LYXBWIYD"},"agent_actions":{"view_html":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN","download_json":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN.json","view_paper":"https://pith.science/paper/LYXBWIYD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.09646&json=true","fetch_graph":"https://pith.science/api/pith-number/LYXBWIYDHTFPMWITGVOGADDBCN/graph.json","fetch_events":"https://pith.science/api/pith-number/LYXBWIYDHTFPMWITGVOGADDBCN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN/action/storage_attestation","attest_author":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN/action/author_attestation","sign_citation":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN/action/citation_signature","submit_replication":"https://pith.science/pith/LYXBWIYDHTFPMWITGVOGADDBCN/action/replication_record"}},"created_at":"2026-07-05T00:05:53.419304+00:00","updated_at":"2026-07-05T00:05:53.419304+00:00"}