{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VAQRABMYEMR44GG53IZSETJH5U","short_pith_number":"pith:VAQRABMY","canonical_record":{"source":{"id":"1808.08437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-25T15:10:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c5f3f23a65642c233dadbedf85946c597559387e6d653e184bd3daf42d801657","abstract_canon_sha256":"563ad3c467495dec2fa5df46d321e96740a5ad439acabb009f069deb81f74e33"},"schema_version":"1.0"},"canonical_sha256":"a8211005982323ce18ddda33224d27ed2d921908291f41e867299bb3ff8e147c","source":{"kind":"arxiv","id":"1808.08437","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08437","created_at":"2026-05-18T00:07:16Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08437v1","created_at":"2026-05-18T00:07:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08437","created_at":"2026-05-18T00:07:16Z"},{"alias_kind":"pith_short_12","alias_value":"VAQRABMYEMR4","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VAQRABMYEMR44GG5","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VAQRABMY","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VAQRABMYEMR44GG53IZSETJH5U","target":"record","payload":{"canonical_record":{"source":{"id":"1808.08437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-25T15:10:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c5f3f23a65642c233dadbedf85946c597559387e6d653e184bd3daf42d801657","abstract_canon_sha256":"563ad3c467495dec2fa5df46d321e96740a5ad439acabb009f069deb81f74e33"},"schema_version":"1.0"},"canonical_sha256":"a8211005982323ce18ddda33224d27ed2d921908291f41e867299bb3ff8e147c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:16.490853Z","signature_b64":"QGr+zwXFwoi5HlsmQ8f51XyYS9eVhw7hoY2ehUnuS43Cdf4DcIrr1uHzvj+cCdI74Q8tHK7+96xhjNj0X9C0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8211005982323ce18ddda33224d27ed2d921908291f41e867299bb3ff8e147c","last_reissued_at":"2026-05-18T00:07:16.490207Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:16.490207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.08437","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cX58izf+z3wNtcGeuw4m69FwZgF0V89MFN4NeLn33bEP5rrOkGB0ZxSh/0wZWd0m7hbLNGPGzVkqKxx1+NerDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T10:28:45.079040Z"},"content_sha256":"09e021890ced4f18c5dc359337c3c5e375b4fa03d27b8a38ad3720f8c8317102","schema_version":"1.0","event_id":"sha256:09e021890ced4f18c5dc359337c3c5e375b4fa03d27b8a38ad3720f8c8317102"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VAQRABMYEMR44GG53IZSETJH5U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Learning for Low-Resource Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jiatao Gu, Kyunghyun Cho, Victor O.K. Li, Yong Wang, Yun Chen","submitted_at":"2018-08-25T15:10:59Z","abstract_excerpt":"In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation~\\citep{gu2018universal} to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08437","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JiNUjJaAxqWCYxRUEJhOw71PoNhZs/8SZigxcr+EbIs8t+k0P1AT2VV1D9x/pRcvbfZgFHOABg+0nUA2OHgbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T10:28:45.079738Z"},"content_sha256":"0d38b6f2dc51c48cab9b10017cb77c9668876cf1386bab91b98adf118b3f8829","schema_version":"1.0","event_id":"sha256:0d38b6f2dc51c48cab9b10017cb77c9668876cf1386bab91b98adf118b3f8829"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VAQRABMYEMR44GG53IZSETJH5U/bundle.json","state_url":"https://pith.science/pith/VAQRABMYEMR44GG53IZSETJH5U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VAQRABMYEMR44GG53IZSETJH5U/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":"2026-05-27T10:28:45Z","links":{"resolver":"https://pith.science/pith/VAQRABMYEMR44GG53IZSETJH5U","bundle":"https://pith.science/pith/VAQRABMYEMR44GG53IZSETJH5U/bundle.json","state":"https://pith.science/pith/VAQRABMYEMR44GG53IZSETJH5U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VAQRABMYEMR44GG53IZSETJH5U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VAQRABMYEMR44GG53IZSETJH5U","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":"563ad3c467495dec2fa5df46d321e96740a5ad439acabb009f069deb81f74e33","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-25T15:10:59Z","title_canon_sha256":"c5f3f23a65642c233dadbedf85946c597559387e6d653e184bd3daf42d801657"},"schema_version":"1.0","source":{"id":"1808.08437","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08437","created_at":"2026-05-18T00:07:16Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08437v1","created_at":"2026-05-18T00:07:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08437","created_at":"2026-05-18T00:07:16Z"},{"alias_kind":"pith_short_12","alias_value":"VAQRABMYEMR4","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VAQRABMYEMR44GG5","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VAQRABMY","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:0d38b6f2dc51c48cab9b10017cb77c9668876cf1386bab91b98adf118b3f8829","target":"graph","created_at":"2026-05-18T00:07:16Z","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":"In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation~\\citep{gu2018universal} to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, ","authors_text":"Jiatao Gu, Kyunghyun Cho, Victor O.K. Li, Yong Wang, Yun Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-25T15:10:59Z","title":"Meta-Learning for Low-Resource Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08437","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":"sha256:09e021890ced4f18c5dc359337c3c5e375b4fa03d27b8a38ad3720f8c8317102","target":"record","created_at":"2026-05-18T00:07:16Z","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":"563ad3c467495dec2fa5df46d321e96740a5ad439acabb009f069deb81f74e33","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-25T15:10:59Z","title_canon_sha256":"c5f3f23a65642c233dadbedf85946c597559387e6d653e184bd3daf42d801657"},"schema_version":"1.0","source":{"id":"1808.08437","kind":"arxiv","version":1}},"canonical_sha256":"a8211005982323ce18ddda33224d27ed2d921908291f41e867299bb3ff8e147c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a8211005982323ce18ddda33224d27ed2d921908291f41e867299bb3ff8e147c","first_computed_at":"2026-05-18T00:07:16.490207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:16.490207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QGr+zwXFwoi5HlsmQ8f51XyYS9eVhw7hoY2ehUnuS43Cdf4DcIrr1uHzvj+cCdI74Q8tHK7+96xhjNj0X9C0Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:16.490853Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.08437","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:09e021890ced4f18c5dc359337c3c5e375b4fa03d27b8a38ad3720f8c8317102","sha256:0d38b6f2dc51c48cab9b10017cb77c9668876cf1386bab91b98adf118b3f8829"],"state_sha256":"c43c654be3d553555734cc30104b11d2400405f47937a44c34aaca65f4eeefcb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rQoZ9hWkUMCD/GIA1FDDSVelXDzwnGGOGsTgLJlGypTR0yXueHA+5ZNOS2blaq/GvyA6/ZbE8iE1fegwD47qAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T10:28:45.083186Z","bundle_sha256":"3f0f7a84a294ec94b21117943b3a299b51155d6c5e5ade07e0dd6603b5953bf8"}}