{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:F7UMHKHDXTBZM3OYJ2LYMXDE2M","short_pith_number":"pith:F7UMHKHD","canonical_record":{"source":{"id":"1410.8206","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-10-30T00:20:31Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"96374c0b1115c8aeca6ad758023bf27ec25d7b2ddac204933eb7583ca27eb58c","abstract_canon_sha256":"c3a3294ed22017da54cfc8482c2167d437c4f6ba58dbadf8d7b5d352a8fcdf1e"},"schema_version":"1.0"},"canonical_sha256":"2fe8c3a8e3bcc3966dd84e97865c64d314c85889d74f00bc105d3497d66c4b2e","source":{"kind":"arxiv","id":"1410.8206","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.8206","created_at":"2026-05-18T01:59:58Z"},{"alias_kind":"arxiv_version","alias_value":"1410.8206v4","created_at":"2026-05-18T01:59:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.8206","created_at":"2026-05-18T01:59:58Z"},{"alias_kind":"pith_short_12","alias_value":"F7UMHKHDXTBZ","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"F7UMHKHDXTBZM3OY","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"F7UMHKHD","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:F7UMHKHDXTBZM3OYJ2LYMXDE2M","target":"record","payload":{"canonical_record":{"source":{"id":"1410.8206","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-10-30T00:20:31Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"96374c0b1115c8aeca6ad758023bf27ec25d7b2ddac204933eb7583ca27eb58c","abstract_canon_sha256":"c3a3294ed22017da54cfc8482c2167d437c4f6ba58dbadf8d7b5d352a8fcdf1e"},"schema_version":"1.0"},"canonical_sha256":"2fe8c3a8e3bcc3966dd84e97865c64d314c85889d74f00bc105d3497d66c4b2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:59:58.289295Z","signature_b64":"fwDMZQdAuXlXYQFVPz/aviQvzWn/akb9LEY36p2yqHEJAqTb4R7EDL7fZPq6HQArozrOH41bTjtaepkTaoqHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fe8c3a8e3bcc3966dd84e97865c64d314c85889d74f00bc105d3497d66c4b2e","last_reissued_at":"2026-05-18T01:59:58.288626Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:59:58.288626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1410.8206","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:59:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"omfYhD2g0Qeq5IKJDAX6O/foKXihBPqeyKz0iE++RTYQ3Rv+JUR58QTft9XJz4CXcmibn9L9I4k1Q9UYmcMCAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T21:40:28.750193Z"},"content_sha256":"2710bf4737dca6090f699ee17d49d197091808b8765fb9315b36e6b0a8bff9f5","schema_version":"1.0","event_id":"sha256:2710bf4737dca6090f699ee17d49d197091808b8765fb9315b36e6b0a8bff9f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:F7UMHKHDXTBZM3OYJ2LYMXDE2M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Addressing the Rare Word Problem in Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"Ilya Sutskever, Minh-Thang Luong, Oriol Vinyals, Quoc V. Le, Wojciech Zaremba","submitted_at":"2014-10-30T00:20:31Z","abstract_excerpt":"Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.8206","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:59:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H9BlDlsTpoFWq89dSHiiYUIcZegWVIy3XKVy33qZY8QWe3ixVNu6/0dRVRl+sKsD6JY33ojzl8ivC5JJqNJMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T21:40:28.750562Z"},"content_sha256":"90b718a210751d04e280493550bff634ecb1b9660e5455cb4b77f953d20062ed","schema_version":"1.0","event_id":"sha256:90b718a210751d04e280493550bff634ecb1b9660e5455cb4b77f953d20062ed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M/bundle.json","state_url":"https://pith.science/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T21:40:28Z","links":{"resolver":"https://pith.science/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M","bundle":"https://pith.science/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M/bundle.json","state":"https://pith.science/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F7UMHKHDXTBZM3OYJ2LYMXDE2M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:F7UMHKHDXTBZM3OYJ2LYMXDE2M","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c3a3294ed22017da54cfc8482c2167d437c4f6ba58dbadf8d7b5d352a8fcdf1e","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-10-30T00:20:31Z","title_canon_sha256":"96374c0b1115c8aeca6ad758023bf27ec25d7b2ddac204933eb7583ca27eb58c"},"schema_version":"1.0","source":{"id":"1410.8206","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.8206","created_at":"2026-05-18T01:59:58Z"},{"alias_kind":"arxiv_version","alias_value":"1410.8206v4","created_at":"2026-05-18T01:59:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.8206","created_at":"2026-05-18T01:59:58Z"},{"alias_kind":"pith_short_12","alias_value":"F7UMHKHDXTBZ","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"F7UMHKHDXTBZM3OY","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"F7UMHKHD","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:90b718a210751d04e280493550bff634ecb1b9660e5455cb4b77f953d20062ed","target":"graph","created_at":"2026-05-18T01:59:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing ","authors_text":"Ilya Sutskever, Minh-Thang Luong, Oriol Vinyals, Quoc V. Le, Wojciech Zaremba","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-10-30T00:20:31Z","title":"Addressing the Rare Word Problem in Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.8206","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2710bf4737dca6090f699ee17d49d197091808b8765fb9315b36e6b0a8bff9f5","target":"record","created_at":"2026-05-18T01:59:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c3a3294ed22017da54cfc8482c2167d437c4f6ba58dbadf8d7b5d352a8fcdf1e","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-10-30T00:20:31Z","title_canon_sha256":"96374c0b1115c8aeca6ad758023bf27ec25d7b2ddac204933eb7583ca27eb58c"},"schema_version":"1.0","source":{"id":"1410.8206","kind":"arxiv","version":4}},"canonical_sha256":"2fe8c3a8e3bcc3966dd84e97865c64d314c85889d74f00bc105d3497d66c4b2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fe8c3a8e3bcc3966dd84e97865c64d314c85889d74f00bc105d3497d66c4b2e","first_computed_at":"2026-05-18T01:59:58.288626Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:59:58.288626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fwDMZQdAuXlXYQFVPz/aviQvzWn/akb9LEY36p2yqHEJAqTb4R7EDL7fZPq6HQArozrOH41bTjtaepkTaoqHBg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:59:58.289295Z","signed_message":"canonical_sha256_bytes"},"source_id":"1410.8206","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2710bf4737dca6090f699ee17d49d197091808b8765fb9315b36e6b0a8bff9f5","sha256:90b718a210751d04e280493550bff634ecb1b9660e5455cb4b77f953d20062ed"],"state_sha256":"a535c2b9eb45dcb7c1f90dbf14ca6c0ca3c6e48ec47b2f973e47686dc77f19dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4V5Z/gdHeqMoQVqkbEPYKlxLkzb/8QCHwRRbrclKTbvZdqPEE1gfR1cbqQW5T7+bMeAttJ6cLhlRXr+BPNAmAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T21:40:28.752433Z","bundle_sha256":"757699a1aa8371be52f3aeeac6c1b1be99d39a15d2cbbe973b56a58d4f5ce70d"}}