{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:NKXVPHLMSP27Y3CPPWFUXYY2L4","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":"be47a46fc512f03a5b88900a5c960d738f3beb725359054cba68ac75c5f54bb3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-05-27T09:27:42Z","title_canon_sha256":"568696e75e34b86441b497bda968652e7cbf0e1fcf1430f4d4ea8fbf32aff869"},"schema_version":"1.0","source":{"id":"2105.13022","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.13022","created_at":"2026-07-05T02:43:56Z"},{"alias_kind":"arxiv_version","alias_value":"2105.13022v1","created_at":"2026-07-05T02:43:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.13022","created_at":"2026-07-05T02:43:56Z"},{"alias_kind":"pith_short_12","alias_value":"NKXVPHLMSP27","created_at":"2026-07-05T02:43:56Z"},{"alias_kind":"pith_short_16","alias_value":"NKXVPHLMSP27Y3CP","created_at":"2026-07-05T02:43:56Z"},{"alias_kind":"pith_short_8","alias_value":"NKXVPHLM","created_at":"2026-07-05T02:43:56Z"}],"graph_snapshots":[{"event_id":"sha256:f8f5662288a8f0e5b5658ba885a3fd92aae79e1d9157e309087a0827e4518131","target":"graph","created_at":"2026-07-05T02:43:56Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2105.13022/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval to improve the translation accuracy. However, the traditional kNN algorithm used in kNN-MT simply retrieves a same number of nearest neighbors for each target token, which may cause prediction errors when the retrieved neighbors include noises. In this paper, we propose Adaptive kNN-MT to dynamically determine the number of k for each target token. We achieve this by introducing a light-weight Meta-k Network, which ","authors_text":"Boxing Chen, Jiajun Chen, Junliang Guo, Shujian Huang, Weihua Luo, Xin Zheng, Zhirui Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-05-27T09:27:42Z","title":"Adaptive Nearest Neighbor Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.13022","kind":"arxiv","version":1},"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":"sha256:98e6962bf20a19041d5f963b186a0fbf579a323496dc6e0a07a7771e7e373684","target":"record","created_at":"2026-07-05T02:43:56Z","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":"be47a46fc512f03a5b88900a5c960d738f3beb725359054cba68ac75c5f54bb3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-05-27T09:27:42Z","title_canon_sha256":"568696e75e34b86441b497bda968652e7cbf0e1fcf1430f4d4ea8fbf32aff869"},"schema_version":"1.0","source":{"id":"2105.13022","kind":"arxiv","version":1}},"canonical_sha256":"6aaf579d6c93f5fc6c4f7d8b4be31a5f38f2b5dc5427adab92f011eb4dfc6050","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6aaf579d6c93f5fc6c4f7d8b4be31a5f38f2b5dc5427adab92f011eb4dfc6050","first_computed_at":"2026-07-05T02:43:56.582666Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:43:56.582666Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0beul63Ai1C9mRgKODNTl2sVPZe5oON8S6kk4X1NVj1Ia8jEHzaNRPnp0RzzNPhHxxQ4njyJH1+/vw9JqRtgAA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:43:56.583111Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.13022","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98e6962bf20a19041d5f963b186a0fbf579a323496dc6e0a07a7771e7e373684","sha256:f8f5662288a8f0e5b5658ba885a3fd92aae79e1d9157e309087a0827e4518131"],"state_sha256":"a91dbce6d4ae82ccce8618b38c3b16cd90698384f745aa52cc22da57f6e0b0c3"}