{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:OKA5AV3NNHY4Q54TCMUL74EJKO","short_pith_number":"pith:OKA5AV3N","canonical_record":{"source":{"id":"1905.01976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-23T15:15:12Z","cross_cats_sorted":[],"title_canon_sha256":"5ef0b81f590c8e06f0b152faee242d142f8458b204ed0cc2ecc59751eadfd764","abstract_canon_sha256":"9cfffddc0bfdd1b2dddc19f983203146414b5f49fb6f8855e754d615c6a0be86"},"schema_version":"1.0"},"canonical_sha256":"7281d0576d69f1c877931328bff08953b2cb620aba59402aa5156d040f9d7ab7","source":{"kind":"arxiv","id":"1905.01976","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01976","created_at":"2026-05-17T23:46:56Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01976v1","created_at":"2026-05-17T23:46:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01976","created_at":"2026-05-17T23:46:56Z"},{"alias_kind":"pith_short_12","alias_value":"OKA5AV3NNHY4","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OKA5AV3NNHY4Q54T","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OKA5AV3N","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:OKA5AV3NNHY4Q54TCMUL74EJKO","target":"record","payload":{"canonical_record":{"source":{"id":"1905.01976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-23T15:15:12Z","cross_cats_sorted":[],"title_canon_sha256":"5ef0b81f590c8e06f0b152faee242d142f8458b204ed0cc2ecc59751eadfd764","abstract_canon_sha256":"9cfffddc0bfdd1b2dddc19f983203146414b5f49fb6f8855e754d615c6a0be86"},"schema_version":"1.0"},"canonical_sha256":"7281d0576d69f1c877931328bff08953b2cb620aba59402aa5156d040f9d7ab7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:56.800548Z","signature_b64":"XNjwjzffvqexWmWbJ/W4vFh2ktX8k8WG7EC0kMpiCkae/jBqAnUEyZuXOr0NLMWAVCl+hohpYbik/dHAcodCAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7281d0576d69f1c877931328bff08953b2cb620aba59402aa5156d040f9d7ab7","last_reissued_at":"2026-05-17T23:46:56.799874Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:56.799874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.01976","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":"2026-05-17T23:46:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5Z+yS2O5BILeVxd/2vi2wkTaDlPLhbaeC3CZ/j6QhUMTQtMg76fZJS+EOWm5GzLViI/Rk22yRTCI7L3MWAhHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T02:12:53.029168Z"},"content_sha256":"68af6a1a7365adae329c444d4c7e0ab51855aef90c066ce4bcdd4547eb36351b","schema_version":"1.0","event_id":"sha256:68af6a1a7365adae329c444d4c7e0ab51855aef90c066ce4bcdd4547eb36351b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:OKA5AV3NNHY4Q54TCMUL74EJKO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Md. Akmal Haidar, Mehdi Rezagholizadeh","submitted_at":"2019-04-23T15:15:12Z","abstract_excerpt":"Text generation is of particular interest in many NLP applications such as machine translation, language modeling, and text summarization. Generative adversarial networks (GANs) achieved a remarkable success in high quality image generation in computer vision,and recently, GANs have gained lots of interest from the NLP community as well. However, achieving similar success in NLP would be more challenging due to the discrete nature of text. In this work, we introduce a method using knowledge distillation to effectively exploit GAN setup for text generation. We demonstrate how autoencoders (AEs)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01976","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":"2026-05-17T23:46:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zKLCASed37xigc7Y2kcAXHg00oNdKEz7mDl+uaOgC3oHxKW0R3vpBJDvoI8Us6+6CEaq2nqF4cjEvKiehjELDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T02:12:53.029886Z"},"content_sha256":"571c59e1f424ae25a478933fa1e9200dbe41f8adc54b7eeb6cd4010440042a08","schema_version":"1.0","event_id":"sha256:571c59e1f424ae25a478933fa1e9200dbe41f8adc54b7eeb6cd4010440042a08"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OKA5AV3NNHY4Q54TCMUL74EJKO/bundle.json","state_url":"https://pith.science/pith/OKA5AV3NNHY4Q54TCMUL74EJKO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OKA5AV3NNHY4Q54TCMUL74EJKO/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":"2026-06-08T02:12:53Z","links":{"resolver":"https://pith.science/pith/OKA5AV3NNHY4Q54TCMUL74EJKO","bundle":"https://pith.science/pith/OKA5AV3NNHY4Q54TCMUL74EJKO/bundle.json","state":"https://pith.science/pith/OKA5AV3NNHY4Q54TCMUL74EJKO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OKA5AV3NNHY4Q54TCMUL74EJKO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OKA5AV3NNHY4Q54TCMUL74EJKO","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":"9cfffddc0bfdd1b2dddc19f983203146414b5f49fb6f8855e754d615c6a0be86","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-23T15:15:12Z","title_canon_sha256":"5ef0b81f590c8e06f0b152faee242d142f8458b204ed0cc2ecc59751eadfd764"},"schema_version":"1.0","source":{"id":"1905.01976","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01976","created_at":"2026-05-17T23:46:56Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01976v1","created_at":"2026-05-17T23:46:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01976","created_at":"2026-05-17T23:46:56Z"},{"alias_kind":"pith_short_12","alias_value":"OKA5AV3NNHY4","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OKA5AV3NNHY4Q54T","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OKA5AV3N","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:571c59e1f424ae25a478933fa1e9200dbe41f8adc54b7eeb6cd4010440042a08","target":"graph","created_at":"2026-05-17T23:46:56Z","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"},"paper":{"abstract_excerpt":"Text generation is of particular interest in many NLP applications such as machine translation, language modeling, and text summarization. Generative adversarial networks (GANs) achieved a remarkable success in high quality image generation in computer vision,and recently, GANs have gained lots of interest from the NLP community as well. However, achieving similar success in NLP would be more challenging due to the discrete nature of text. In this work, we introduce a method using knowledge distillation to effectively exploit GAN setup for text generation. We demonstrate how autoencoders (AEs)","authors_text":"Md. Akmal Haidar, Mehdi Rezagholizadeh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-23T15:15:12Z","title":"TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01976","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":"sha256:68af6a1a7365adae329c444d4c7e0ab51855aef90c066ce4bcdd4547eb36351b","target":"record","created_at":"2026-05-17T23:46:56Z","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":"9cfffddc0bfdd1b2dddc19f983203146414b5f49fb6f8855e754d615c6a0be86","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-23T15:15:12Z","title_canon_sha256":"5ef0b81f590c8e06f0b152faee242d142f8458b204ed0cc2ecc59751eadfd764"},"schema_version":"1.0","source":{"id":"1905.01976","kind":"arxiv","version":1}},"canonical_sha256":"7281d0576d69f1c877931328bff08953b2cb620aba59402aa5156d040f9d7ab7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7281d0576d69f1c877931328bff08953b2cb620aba59402aa5156d040f9d7ab7","first_computed_at":"2026-05-17T23:46:56.799874Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:56.799874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XNjwjzffvqexWmWbJ/W4vFh2ktX8k8WG7EC0kMpiCkae/jBqAnUEyZuXOr0NLMWAVCl+hohpYbik/dHAcodCAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:56.800548Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.01976","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68af6a1a7365adae329c444d4c7e0ab51855aef90c066ce4bcdd4547eb36351b","sha256:571c59e1f424ae25a478933fa1e9200dbe41f8adc54b7eeb6cd4010440042a08"],"state_sha256":"84a05d50c7deb66836a07619e13287733accec6a54a4bf0fef1cb828a35213c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dJVUf5b3nYMQsVpBOB5At7XgJhh8GOjrnWXJc+29lEY4AoVZ3UoZ10TXaGk1ofHkml1ePO+0NIDFcHxNQ/KSAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T02:12:53.033933Z","bundle_sha256":"80a6d827f57fa8cf0d12d7404c9dcd730c7a09669ec613bdfe661cea324106f5"}}