{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PSBXHPVVY5XVAC4X73DQYBTVMW","short_pith_number":"pith:PSBXHPVV","schema_version":"1.0","canonical_sha256":"7c8373beb5c76f500b97fec70c067565bc7f54e61cb04ae57c677b4a5ef21516","source":{"kind":"arxiv","id":"1810.13320","version":2},"attestation_state":"computed","paper":{"title":"Convolutional Self-Attention Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baosong Yang, Derek F. Wong, Lidia S. Chao, Longyue Wang, Zhaopeng Tu","submitted_at":"2018-10-31T14:58:30Z","abstract_excerpt":"Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies. It can be further enhanced with multi-headed attention mechanism by allowing the model to jointly attend to information from different representation subspaces at different positions (Vaswani et al., 2017). In this work, we propose a novel convolutional self-attention network (CSAN), which offers SAN the abilities to 1) capture neighboring dependencies, and 2) model the interaction between multiple attention heads. Experimental results on W"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1810.13320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-31T14:58:30Z","cross_cats_sorted":[],"title_canon_sha256":"df202341adf99fb14a5b37305ce476b0179e1db95b42ed80b2796d16de340ee6","abstract_canon_sha256":"5082bbe7cb8898cf14618ef1fc574791fcc0fdebbd92789b232ee40f0d4c64a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:11.325550Z","signature_b64":"pR212crTKHSEhbZ3lwm4+aW7Uh2Hu+8b+TL7hSVR/NQTQOF6BQwqo+VmAZ0YLZFUBWaFPXD7i2P9Qk4yljN+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c8373beb5c76f500b97fec70c067565bc7f54e61cb04ae57c677b4a5ef21516","last_reissued_at":"2026-05-17T23:49:11.324925Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:11.324925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convolutional Self-Attention Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baosong Yang, Derek F. Wong, Lidia S. Chao, Longyue Wang, Zhaopeng Tu","submitted_at":"2018-10-31T14:58:30Z","abstract_excerpt":"Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies. It can be further enhanced with multi-headed attention mechanism by allowing the model to jointly attend to information from different representation subspaces at different positions (Vaswani et al., 2017). In this work, we propose a novel convolutional self-attention network (CSAN), which offers SAN the abilities to 1) capture neighboring dependencies, and 2) model the interaction between multiple attention heads. Experimental results on W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13320","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1810.13320","created_at":"2026-05-17T23:49:11.325010+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.13320v2","created_at":"2026-05-17T23:49:11.325010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13320","created_at":"2026-05-17T23:49:11.325010+00:00"},{"alias_kind":"pith_short_12","alias_value":"PSBXHPVVY5XV","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PSBXHPVVY5XVAC4X","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PSBXHPVV","created_at":"2026-05-18T12:32:46.962924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW","json":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW.json","graph_json":"https://pith.science/api/pith-number/PSBXHPVVY5XVAC4X73DQYBTVMW/graph.json","events_json":"https://pith.science/api/pith-number/PSBXHPVVY5XVAC4X73DQYBTVMW/events.json","paper":"https://pith.science/paper/PSBXHPVV"},"agent_actions":{"view_html":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW","download_json":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW.json","view_paper":"https://pith.science/paper/PSBXHPVV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.13320&json=true","fetch_graph":"https://pith.science/api/pith-number/PSBXHPVVY5XVAC4X73DQYBTVMW/graph.json","fetch_events":"https://pith.science/api/pith-number/PSBXHPVVY5XVAC4X73DQYBTVMW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW/action/storage_attestation","attest_author":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW/action/author_attestation","sign_citation":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW/action/citation_signature","submit_replication":"https://pith.science/pith/PSBXHPVVY5XVAC4X73DQYBTVMW/action/replication_record"}},"created_at":"2026-05-17T23:49:11.325010+00:00","updated_at":"2026-05-17T23:49:11.325010+00:00"}