{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:UGTVEZRYUDD2WWGRVR34VHLM3H","short_pith_number":"pith:UGTVEZRY","canonical_record":{"source":{"id":"1812.04405","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-11T14:05:24Z","cross_cats_sorted":[],"title_canon_sha256":"bee2082db331d605a71825f0d1fd400940e384b54836d749676433d6b4c08f6c","abstract_canon_sha256":"7473d9ceeaab4cdaa98d94afa831fe1dc26ac7b561cfd017578a43a6b9a5bc1a"},"schema_version":"1.0"},"canonical_sha256":"a1a7526638a0c7ab58d1ac77ca9d6cd9f8060f1e77060fcfe922d5dabb7f8bb8","source":{"kind":"arxiv","id":"1812.04405","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.04405","created_at":"2026-05-17T23:58:32Z"},{"alias_kind":"arxiv_version","alias_value":"1812.04405v1","created_at":"2026-05-17T23:58:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04405","created_at":"2026-05-17T23:58:32Z"},{"alias_kind":"pith_short_12","alias_value":"UGTVEZRYUDD2","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UGTVEZRYUDD2WWGR","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UGTVEZRY","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:UGTVEZRYUDD2WWGRVR34VHLM3H","target":"record","payload":{"canonical_record":{"source":{"id":"1812.04405","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-11T14:05:24Z","cross_cats_sorted":[],"title_canon_sha256":"bee2082db331d605a71825f0d1fd400940e384b54836d749676433d6b4c08f6c","abstract_canon_sha256":"7473d9ceeaab4cdaa98d94afa831fe1dc26ac7b561cfd017578a43a6b9a5bc1a"},"schema_version":"1.0"},"canonical_sha256":"a1a7526638a0c7ab58d1ac77ca9d6cd9f8060f1e77060fcfe922d5dabb7f8bb8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:32.275857Z","signature_b64":"quF/MLEkOaeCiBYpl+mxirI/mkRWbdsNpkokeFahH0+Bwa4tZ24WCbmDIHn8iYLPGK8SB6myRPaudacPU137Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1a7526638a0c7ab58d1ac77ca9d6cd9f8060f1e77060fcfe922d5dabb7f8bb8","last_reissued_at":"2026-05-17T23:58:32.275093Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:32.275093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.04405","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":"2026-05-17T23:58:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qaU4DXnibmr1BXF7WVuwoLg6xAi0PaX33UxdVmNGNSl1eV55yBfQl83p1TCyoVi4MEbsCTZ/7M2Qjp8K67sADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T10:25:46.520702Z"},"content_sha256":"a680d0d6b80e721431d2d820762349847e54912c758fa3536e57d698a2770f43","schema_version":"1.0","event_id":"sha256:a680d0d6b80e721431d2d820762349847e54912c758fa3536e57d698a2770f43"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:UGTVEZRYUDD2WWGRVR34VHLM3H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conditional Variational Autoencoder for Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Artidoro Pagnoni, Kevin Liu, Shangyan Li","submitted_at":"2018-12-11T14:05:24Z","abstract_excerpt":"We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. We extend this model with a co-attention mechanism motivated by Parikh et al. in the inference network. Compared to the vision domain, latent variable models for text face additional challenges due to the discrete nature of language, namely posterior collapse. We experiment with different approaches to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04405","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":"2026-05-17T23:58:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QWzAgUp5gKk5DZrY5rnHtlXcupbSj49dv4nhv1Y3Vh+4WhDQjok0gwD2vf21qLvGWN53xTAQCBznEHyvO8S3Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T10:25:46.521044Z"},"content_sha256":"5442f758ec1f542ff3604e11f934bd6b6ff34952fcbabe2eb871a0dcdacbc2f2","schema_version":"1.0","event_id":"sha256:5442f758ec1f542ff3604e11f934bd6b6ff34952fcbabe2eb871a0dcdacbc2f2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UGTVEZRYUDD2WWGRVR34VHLM3H/bundle.json","state_url":"https://pith.science/pith/UGTVEZRYUDD2WWGRVR34VHLM3H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UGTVEZRYUDD2WWGRVR34VHLM3H/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":"2026-06-12T10:25:46Z","links":{"resolver":"https://pith.science/pith/UGTVEZRYUDD2WWGRVR34VHLM3H","bundle":"https://pith.science/pith/UGTVEZRYUDD2WWGRVR34VHLM3H/bundle.json","state":"https://pith.science/pith/UGTVEZRYUDD2WWGRVR34VHLM3H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UGTVEZRYUDD2WWGRVR34VHLM3H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:UGTVEZRYUDD2WWGRVR34VHLM3H","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":"7473d9ceeaab4cdaa98d94afa831fe1dc26ac7b561cfd017578a43a6b9a5bc1a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-11T14:05:24Z","title_canon_sha256":"bee2082db331d605a71825f0d1fd400940e384b54836d749676433d6b4c08f6c"},"schema_version":"1.0","source":{"id":"1812.04405","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.04405","created_at":"2026-05-17T23:58:32Z"},{"alias_kind":"arxiv_version","alias_value":"1812.04405v1","created_at":"2026-05-17T23:58:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04405","created_at":"2026-05-17T23:58:32Z"},{"alias_kind":"pith_short_12","alias_value":"UGTVEZRYUDD2","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UGTVEZRYUDD2WWGR","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UGTVEZRY","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:5442f758ec1f542ff3604e11f934bd6b6ff34952fcbabe2eb871a0dcdacbc2f2","target":"graph","created_at":"2026-05-17T23:58:32Z","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":"We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. We extend this model with a co-attention mechanism motivated by Parikh et al. in the inference network. Compared to the vision domain, latent variable models for text face additional challenges due to the discrete nature of language, namely posterior collapse. We experiment with different approaches to ","authors_text":"Artidoro Pagnoni, Kevin Liu, Shangyan Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-11T14:05:24Z","title":"Conditional Variational Autoencoder for Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04405","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:a680d0d6b80e721431d2d820762349847e54912c758fa3536e57d698a2770f43","target":"record","created_at":"2026-05-17T23:58:32Z","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":"7473d9ceeaab4cdaa98d94afa831fe1dc26ac7b561cfd017578a43a6b9a5bc1a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-11T14:05:24Z","title_canon_sha256":"bee2082db331d605a71825f0d1fd400940e384b54836d749676433d6b4c08f6c"},"schema_version":"1.0","source":{"id":"1812.04405","kind":"arxiv","version":1}},"canonical_sha256":"a1a7526638a0c7ab58d1ac77ca9d6cd9f8060f1e77060fcfe922d5dabb7f8bb8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a1a7526638a0c7ab58d1ac77ca9d6cd9f8060f1e77060fcfe922d5dabb7f8bb8","first_computed_at":"2026-05-17T23:58:32.275093Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:32.275093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"quF/MLEkOaeCiBYpl+mxirI/mkRWbdsNpkokeFahH0+Bwa4tZ24WCbmDIHn8iYLPGK8SB6myRPaudacPU137Bg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:32.275857Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.04405","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a680d0d6b80e721431d2d820762349847e54912c758fa3536e57d698a2770f43","sha256:5442f758ec1f542ff3604e11f934bd6b6ff34952fcbabe2eb871a0dcdacbc2f2"],"state_sha256":"88ef6c931cbe11bb8521f442e4edccc5070c82839d7ef6b46702bef6f969276c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZYLSKdlub7Ey9w0pYUvcMiodEh2TqHAOQSHThqFsjDx8WqxCgexwwFp9CIO6gEt9spUyyqlF7LGgV6YvyztaAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T10:25:46.523042Z","bundle_sha256":"b68c4cd1fb8122cb2a770678d3ad7482b6cff273b563a42a3271864d9b138850"}}