{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:KHG3AFTFA26Z5HSEAUCPHGQR2W","short_pith_number":"pith:KHG3AFTF","canonical_record":{"source":{"id":"1912.00544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-12-02T02:08:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"659736cb1e4addfd95f7f0e0c8579f6b49aeb6f9caf1144cb9241154a505826f","abstract_canon_sha256":"01ad8e708dbeb184f7fed7ee0d85048253362565696891c9760c1a87f7c51705"},"schema_version":"1.0"},"canonical_sha256":"51cdb0166506bd9e9e440504f39a11d5a1065430da971b1b6787af948665409a","source":{"kind":"arxiv","id":"1912.00544","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.00544","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"arxiv_version","alias_value":"1912.00544v1","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.00544","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"pith_short_12","alias_value":"KHG3AFTFA26Z","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"pith_short_16","alias_value":"KHG3AFTFA26Z5HSE","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"pith_short_8","alias_value":"KHG3AFTF","created_at":"2026-07-05T00:23:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:KHG3AFTFA26Z5HSEAUCPHGQR2W","target":"record","payload":{"canonical_record":{"source":{"id":"1912.00544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-12-02T02:08:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"659736cb1e4addfd95f7f0e0c8579f6b49aeb6f9caf1144cb9241154a505826f","abstract_canon_sha256":"01ad8e708dbeb184f7fed7ee0d85048253362565696891c9760c1a87f7c51705"},"schema_version":"1.0"},"canonical_sha256":"51cdb0166506bd9e9e440504f39a11d5a1065430da971b1b6787af948665409a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:23:14.739487Z","signature_b64":"3oaDGdiI8aLB8hTzQ84PaQJycQNDZdprVBzN6sgMyLECg//p8U26M+zJT2FPrsyPBauDqE5+rFPKK+7YnkwlAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51cdb0166506bd9e9e440504f39a11d5a1065430da971b1b6787af948665409a","last_reissued_at":"2026-07-05T00:23:14.739133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:23:14.739133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.00544","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":"2026-07-05T00:23:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KYrfk2VaPNj9pe9ur9+OW3dMJadbb5qEU/zxsMnt059eSzEIcGT4G5PfkABqmr8gYEWPae2UFnsDJkhLbaaSBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:59:51.514070Z"},"content_sha256":"f4bfb7d009e2f43a41331a275c9a17e3ad81b13e650169a18f5d941a521ee7c8","schema_version":"1.0","event_id":"sha256:f4bfb7d009e2f43a41331a275c9a17e3ad81b13e650169a18f5d941a521ee7c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:KHG3AFTFA26Z5HSEAUCPHGQR2W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Scale Self-Attention for Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Pengfei Liu, Qipeng Guo, Xiangyang Xue, Xipeng Qiu, Zheng Zhang","submitted_at":"2019-12-02T02:08:00Z","abstract_excerpt":"In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules. We propose a Multi-Scale Transformer which uses multi-scale multi-head self-attention to capture features from different scales. Based on the linguistic perspective and the analysis of pre-trained Transformer (BERT) on a huge corpus, we further design a strategy to control the scale distribution for each layer. Results of three different kinds of tasks (21 datasets) show our Multi-Scale Transformer outperforms the standard Transformer consistently and significantly on small and moderate size da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.00544","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/1912.00544/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":"2026-07-05T00:23:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eWNMnabjDQ+vWiJBpCKxpS7zx/kOvKgNAoXyD89TAiYwBB03Lhol0dXnO9RYjO5xlAoflAdCfVdCyxDLizozAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:59:51.514562Z"},"content_sha256":"15e700e69c00bf1f93da9a42087eba0bb8a29530df23181b43001eba175441f1","schema_version":"1.0","event_id":"sha256:15e700e69c00bf1f93da9a42087eba0bb8a29530df23181b43001eba175441f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W/bundle.json","state_url":"https://pith.science/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W/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":"2026-08-05T23:59:51Z","links":{"resolver":"https://pith.science/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W","bundle":"https://pith.science/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W/bundle.json","state":"https://pith.science/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KHG3AFTFA26Z5HSEAUCPHGQR2W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:KHG3AFTFA26Z5HSEAUCPHGQR2W","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":"01ad8e708dbeb184f7fed7ee0d85048253362565696891c9760c1a87f7c51705","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-12-02T02:08:00Z","title_canon_sha256":"659736cb1e4addfd95f7f0e0c8579f6b49aeb6f9caf1144cb9241154a505826f"},"schema_version":"1.0","source":{"id":"1912.00544","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.00544","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"arxiv_version","alias_value":"1912.00544v1","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.00544","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"pith_short_12","alias_value":"KHG3AFTFA26Z","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"pith_short_16","alias_value":"KHG3AFTFA26Z5HSE","created_at":"2026-07-05T00:23:14Z"},{"alias_kind":"pith_short_8","alias_value":"KHG3AFTF","created_at":"2026-07-05T00:23:14Z"}],"graph_snapshots":[{"event_id":"sha256:15e700e69c00bf1f93da9a42087eba0bb8a29530df23181b43001eba175441f1","target":"graph","created_at":"2026-07-05T00:23:14Z","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/1912.00544/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules. We propose a Multi-Scale Transformer which uses multi-scale multi-head self-attention to capture features from different scales. Based on the linguistic perspective and the analysis of pre-trained Transformer (BERT) on a huge corpus, we further design a strategy to control the scale distribution for each layer. Results of three different kinds of tasks (21 datasets) show our Multi-Scale Transformer outperforms the standard Transformer consistently and significantly on small and moderate size da","authors_text":"Pengfei Liu, Qipeng Guo, Xiangyang Xue, Xipeng Qiu, Zheng Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-12-02T02:08:00Z","title":"Multi-Scale Self-Attention for Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.00544","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:f4bfb7d009e2f43a41331a275c9a17e3ad81b13e650169a18f5d941a521ee7c8","target":"record","created_at":"2026-07-05T00:23:14Z","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":"01ad8e708dbeb184f7fed7ee0d85048253362565696891c9760c1a87f7c51705","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-12-02T02:08:00Z","title_canon_sha256":"659736cb1e4addfd95f7f0e0c8579f6b49aeb6f9caf1144cb9241154a505826f"},"schema_version":"1.0","source":{"id":"1912.00544","kind":"arxiv","version":1}},"canonical_sha256":"51cdb0166506bd9e9e440504f39a11d5a1065430da971b1b6787af948665409a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"51cdb0166506bd9e9e440504f39a11d5a1065430da971b1b6787af948665409a","first_computed_at":"2026-07-05T00:23:14.739133Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:23:14.739133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3oaDGdiI8aLB8hTzQ84PaQJycQNDZdprVBzN6sgMyLECg//p8U26M+zJT2FPrsyPBauDqE5+rFPKK+7YnkwlAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:23:14.739487Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.00544","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4bfb7d009e2f43a41331a275c9a17e3ad81b13e650169a18f5d941a521ee7c8","sha256:15e700e69c00bf1f93da9a42087eba0bb8a29530df23181b43001eba175441f1"],"state_sha256":"2f0cdca427966d5a363fe294ad76af6c1897ab0323c9a4a72f42b5a91d43d290"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mYpLuT/o02DgNVMypNEzFh+Ho+9fCO1EkVGYyDw4tkaMiApc60YmVCKgtfiFtvNU8wA+EiQ4ZeqH+7Sk47wEDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T23:59:51.517945Z","bundle_sha256":"474a42a356fdbb97d3f5d147d0a32eae65d68af93fb48daaf8226f540b52a3ad"}}