{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:47UGJRLNRJL5ZRKMK3Q3IYACJS","short_pith_number":"pith:47UGJRLN","canonical_record":{"source":{"id":"2006.03701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-05T21:36:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b0de75c62d83f28b5521fd042b52a65ac1c94e7aede06d92137517fc7e50349c","abstract_canon_sha256":"ef7f87655bf38bfaad1539914871ebf7945cb84743836976a93786d8b3b5bf0c"},"schema_version":"1.0"},"canonical_sha256":"e7e864c56d8a57dcc54c56e1b460024cbc3b3c1635266705d3e50be56f279d8a","source":{"kind":"arxiv","id":"2006.03701","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.03701","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"arxiv_version","alias_value":"2006.03701v1","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.03701","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"pith_short_12","alias_value":"47UGJRLNRJL5","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"pith_short_16","alias_value":"47UGJRLNRJL5ZRKM","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"pith_short_8","alias_value":"47UGJRLN","created_at":"2026-07-05T01:08:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:47UGJRLNRJL5ZRKMK3Q3IYACJS","target":"record","payload":{"canonical_record":{"source":{"id":"2006.03701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-05T21:36:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b0de75c62d83f28b5521fd042b52a65ac1c94e7aede06d92137517fc7e50349c","abstract_canon_sha256":"ef7f87655bf38bfaad1539914871ebf7945cb84743836976a93786d8b3b5bf0c"},"schema_version":"1.0"},"canonical_sha256":"e7e864c56d8a57dcc54c56e1b460024cbc3b3c1635266705d3e50be56f279d8a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:32.172125Z","signature_b64":"bdEejksmM5ewPY2hUk6xye6uqRhBGjCfYdExuoVrc1OfY7ot5aNze2xhE1iPc827hphvswGwa6Gib2G4qv30Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7e864c56d8a57dcc54c56e1b460024cbc3b3c1635266705d3e50be56f279d8a","last_reissued_at":"2026-07-05T01:08:32.171680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:32.171680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.03701","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":"2026-07-05T01:08:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KgtXguhEgBKOAUKtq31wVbz4qmRcmmckgeYSHSVtjxuE2+Ztrk6Q0vEf5n7iynvWS0S8qfjyyXSUx4DFvjs0BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:29:09.714956Z"},"content_sha256":"f2c59e3cbb7848cc02d6b6e572b13b3322445975a97722cdd568694f81c35bc2","schema_version":"1.0","event_id":"sha256:f2c59e3cbb7848cc02d6b6e572b13b3322445975a97722cdd568694f81c35bc2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:47UGJRLNRJL5ZRKMK3Q3IYACJS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Natural Language Understanding in Task-Oriented Dialog","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Ojas Ahuja, Shrey Desai","submitted_at":"2020-06-05T21:36:33Z","abstract_excerpt":"Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural language understanding benchmarks. However, these models frequently have in excess of tens of millions of parameters, making them impossible to deploy on-device where resource-efficiency is a major concern. In this work, we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips, with under 100K parameters. Moreover, we perform acceleration "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.03701","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.03701/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":"2026-07-05T01:08:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ii9n4jNrOnNqkDXzaMAcuL2+v1lvr02hkHGMPgrtARwiZYLTUBjn+ppj6uX7waBhNOuiYgn8fDt3RGQUvvoJDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:29:09.715489Z"},"content_sha256":"367b91a54b77679c07297218486376eab1efe173c4c5eb1fcb10dfe2c8d22a26","schema_version":"1.0","event_id":"sha256:367b91a54b77679c07297218486376eab1efe173c4c5eb1fcb10dfe2c8d22a26"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS/bundle.json","state_url":"https://pith.science/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS/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":"2026-08-06T16:29:09Z","links":{"resolver":"https://pith.science/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS","bundle":"https://pith.science/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS/bundle.json","state":"https://pith.science/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/47UGJRLNRJL5ZRKMK3Q3IYACJS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:47UGJRLNRJL5ZRKMK3Q3IYACJS","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":"ef7f87655bf38bfaad1539914871ebf7945cb84743836976a93786d8b3b5bf0c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-05T21:36:33Z","title_canon_sha256":"b0de75c62d83f28b5521fd042b52a65ac1c94e7aede06d92137517fc7e50349c"},"schema_version":"1.0","source":{"id":"2006.03701","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.03701","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"arxiv_version","alias_value":"2006.03701v1","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.03701","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"pith_short_12","alias_value":"47UGJRLNRJL5","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"pith_short_16","alias_value":"47UGJRLNRJL5ZRKM","created_at":"2026-07-05T01:08:32Z"},{"alias_kind":"pith_short_8","alias_value":"47UGJRLN","created_at":"2026-07-05T01:08:32Z"}],"graph_snapshots":[{"event_id":"sha256:367b91a54b77679c07297218486376eab1efe173c4c5eb1fcb10dfe2c8d22a26","target":"graph","created_at":"2026-07-05T01:08:32Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.03701/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural language understanding benchmarks. However, these models frequently have in excess of tens of millions of parameters, making them impossible to deploy on-device where resource-efficiency is a major concern. In this work, we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips, with under 100K parameters. Moreover, we perform acceleration ","authors_text":"Ojas Ahuja, Shrey Desai","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-05T21:36:33Z","title":"Accelerating Natural Language Understanding in Task-Oriented Dialog"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.03701","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:f2c59e3cbb7848cc02d6b6e572b13b3322445975a97722cdd568694f81c35bc2","target":"record","created_at":"2026-07-05T01:08:32Z","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":"ef7f87655bf38bfaad1539914871ebf7945cb84743836976a93786d8b3b5bf0c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-05T21:36:33Z","title_canon_sha256":"b0de75c62d83f28b5521fd042b52a65ac1c94e7aede06d92137517fc7e50349c"},"schema_version":"1.0","source":{"id":"2006.03701","kind":"arxiv","version":1}},"canonical_sha256":"e7e864c56d8a57dcc54c56e1b460024cbc3b3c1635266705d3e50be56f279d8a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e7e864c56d8a57dcc54c56e1b460024cbc3b3c1635266705d3e50be56f279d8a","first_computed_at":"2026-07-05T01:08:32.171680Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:08:32.171680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bdEejksmM5ewPY2hUk6xye6uqRhBGjCfYdExuoVrc1OfY7ot5aNze2xhE1iPc827hphvswGwa6Gib2G4qv30Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:08:32.172125Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.03701","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2c59e3cbb7848cc02d6b6e572b13b3322445975a97722cdd568694f81c35bc2","sha256:367b91a54b77679c07297218486376eab1efe173c4c5eb1fcb10dfe2c8d22a26"],"state_sha256":"5d5a33542fd361ac6f79b23576d170aa96ba05ceb75d9982d8b7f5519777da9d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KnUe/COwzh1UXYXnRuA6LGP6glHtg9yLg1PCNO47PBkznDeC3TnLENjzj0EZVylnGynTMo2VPono0vL7eagoAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:29:09.721955Z","bundle_sha256":"7fcf40ea2927f103ddda38dfd1333d1b6683006bbfadc6b13b07684dbf124d12"}}