{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:QSQBE5BXHFIKGJVDAEJF7EF6HZ","short_pith_number":"pith:QSQBE5BX","canonical_record":{"source":{"id":"2012.07280","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-12-14T06:20:27Z","cross_cats_sorted":[],"title_canon_sha256":"d13ea7094a7af95aad597d0311f6c7495c9dbad957ac72191f1b98740645cc99","abstract_canon_sha256":"0681f70838d413e0ac280346f705b072c5f38b24e3e1dcafc73b5f855a55b4d5"},"schema_version":"1.0"},"canonical_sha256":"84a01274373950a326a301125f90be3e5dad8b82cd23ac3350087c7262b7a27b","source":{"kind":"arxiv","id":"2012.07280","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.07280","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"arxiv_version","alias_value":"2012.07280v6","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.07280","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"pith_short_12","alias_value":"QSQBE5BXHFIK","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"pith_short_16","alias_value":"QSQBE5BXHFIKGJVD","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"pith_short_8","alias_value":"QSQBE5BX","created_at":"2026-07-05T02:21:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:QSQBE5BXHFIKGJVDAEJF7EF6HZ","target":"record","payload":{"canonical_record":{"source":{"id":"2012.07280","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-12-14T06:20:27Z","cross_cats_sorted":[],"title_canon_sha256":"d13ea7094a7af95aad597d0311f6c7495c9dbad957ac72191f1b98740645cc99","abstract_canon_sha256":"0681f70838d413e0ac280346f705b072c5f38b24e3e1dcafc73b5f855a55b4d5"},"schema_version":"1.0"},"canonical_sha256":"84a01274373950a326a301125f90be3e5dad8b82cd23ac3350087c7262b7a27b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:21:54.237348Z","signature_b64":"fMUloL6MpEGRlyo2gQY4JqF65PhGX34PLk/WkuNU62HWDZSnL2RsCk+256MNyZLdiPT9CNwVhy5Rd6U5neWLCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84a01274373950a326a301125f90be3e5dad8b82cd23ac3350087c7262b7a27b","last_reissued_at":"2026-07-05T02:21:54.236902Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:21:54.236902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.07280","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:21:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lIJyHqVmeQOdS/ybTv83yDeIlsNfKdsLhkk3jOkM4dwgnybTR/m4eJfmDzmMh37AgSkMAH+18RT/tD3ldMzaDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:17:51.457267Z"},"content_sha256":"56137ff92288e7ceb8f3eb8af9a574df774a00cba745acf1108ffa80998e35cf","schema_version":"1.0","event_id":"sha256:56137ff92288e7ceb8f3eb8af9a574df774a00cba745acf1108ffa80998e35cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:QSQBE5BXHFIKGJVDAEJF7EF6HZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contrastive Learning with Adversarial Perturbations for Conditional Text Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dong Bok Lee, Seanie Lee, Sung Ju Hwang","submitted_at":"2020-12-14T06:20:27Z","abstract_excerpt":"Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the \"exposure bias\" problem. In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.07280","kind":"arxiv","version":6},"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/2012.07280/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:21:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"khuWC1ATVkYfQgzDcaBWLY/CktzsG3Ed3V4Rp48wKzNSkt8dUrBUOmFUTJktjTSvCUab7mD34BLgSwWbOKRkDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:17:51.457805Z"},"content_sha256":"06f1c9382a199d13eb39599033c055367eb1a945920acac188bc15a6e7c75a88","schema_version":"1.0","event_id":"sha256:06f1c9382a199d13eb39599033c055367eb1a945920acac188bc15a6e7c75a88"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ/bundle.json","state_url":"https://pith.science/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ/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":"2026-08-14T16:17:51Z","links":{"resolver":"https://pith.science/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ","bundle":"https://pith.science/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ/bundle.json","state":"https://pith.science/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QSQBE5BXHFIKGJVDAEJF7EF6HZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:QSQBE5BXHFIKGJVDAEJF7EF6HZ","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":"0681f70838d413e0ac280346f705b072c5f38b24e3e1dcafc73b5f855a55b4d5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-12-14T06:20:27Z","title_canon_sha256":"d13ea7094a7af95aad597d0311f6c7495c9dbad957ac72191f1b98740645cc99"},"schema_version":"1.0","source":{"id":"2012.07280","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.07280","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"arxiv_version","alias_value":"2012.07280v6","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.07280","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"pith_short_12","alias_value":"QSQBE5BXHFIK","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"pith_short_16","alias_value":"QSQBE5BXHFIKGJVD","created_at":"2026-07-05T02:21:54Z"},{"alias_kind":"pith_short_8","alias_value":"QSQBE5BX","created_at":"2026-07-05T02:21:54Z"}],"graph_snapshots":[{"event_id":"sha256:06f1c9382a199d13eb39599033c055367eb1a945920acac188bc15a6e7c75a88","target":"graph","created_at":"2026-07-05T02:21:54Z","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/2012.07280/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the \"exposure bias\" problem. In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such","authors_text":"Dong Bok Lee, Seanie Lee, Sung Ju Hwang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-12-14T06:20:27Z","title":"Contrastive Learning with Adversarial Perturbations for Conditional Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.07280","kind":"arxiv","version":6},"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":"sha256:56137ff92288e7ceb8f3eb8af9a574df774a00cba745acf1108ffa80998e35cf","target":"record","created_at":"2026-07-05T02:21:54Z","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":"0681f70838d413e0ac280346f705b072c5f38b24e3e1dcafc73b5f855a55b4d5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2020-12-14T06:20:27Z","title_canon_sha256":"d13ea7094a7af95aad597d0311f6c7495c9dbad957ac72191f1b98740645cc99"},"schema_version":"1.0","source":{"id":"2012.07280","kind":"arxiv","version":6}},"canonical_sha256":"84a01274373950a326a301125f90be3e5dad8b82cd23ac3350087c7262b7a27b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"84a01274373950a326a301125f90be3e5dad8b82cd23ac3350087c7262b7a27b","first_computed_at":"2026-07-05T02:21:54.236902Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:21:54.236902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fMUloL6MpEGRlyo2gQY4JqF65PhGX34PLk/WkuNU62HWDZSnL2RsCk+256MNyZLdiPT9CNwVhy5Rd6U5neWLCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:21:54.237348Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.07280","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56137ff92288e7ceb8f3eb8af9a574df774a00cba745acf1108ffa80998e35cf","sha256:06f1c9382a199d13eb39599033c055367eb1a945920acac188bc15a6e7c75a88"],"state_sha256":"7b2f29ce271d9c28f225f034fe828ff9780f77749862e124ac2dc731b69f8474"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jgCzp0k5fNMspnJ42SLHSld11lB6bKgCbHX5+s6Q6m5p5MyBbSX7qAVRBBtaxYoYyYBFEzWgHsCH4a+VRzsTDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T16:17:51.469792Z","bundle_sha256":"fbf6a10d35d149a813b0b2c90f1d4caa2dd07dd9257a063ba5a8c2cacee08f07"}}