{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ALU7SZSEYVKGXLJCDR53XSFEBU","short_pith_number":"pith:ALU7SZSE","schema_version":"1.0","canonical_sha256":"02e9f96644c5546bad221c7bbbc8a40d26b367611960803649ffeb95b93a0674","source":{"kind":"arxiv","id":"1906.04393","version":1},"attestation_state":"computed","paper":{"title":"Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Aston Zhang, Jie Fu, Jinfeng Rao, Luu Anh Tuan, Shuai Zhang, Shuohang Wang, Siu Cheung Hui, Yi Tay","submitted_at":"2019-06-11T04:56:17Z","abstract_excerpt":"Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly ($75\\%$) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Qua"},"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":"1906.04393","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-11T04:56:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a9e1569cc5d14f5d8c0a163c41ec096466c533eb7c52e73e2d16480b36faab96","abstract_canon_sha256":"dc7e361d9286286d69209e9e4b7d22f0c1b6536c8407862789aeb631829dab6a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:39.404878Z","signature_b64":"GoH/El95vFz5gbRt7N6NfF7JWtC3y5ZAo+lKTFamMeVXoVV1nfncbB7ZMJMfRyh28uIvdlh/wBaTtdiaiIXaAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02e9f96644c5546bad221c7bbbc8a40d26b367611960803649ffeb95b93a0674","last_reissued_at":"2026-05-17T23:43:39.404226Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:39.404226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Aston Zhang, Jie Fu, Jinfeng Rao, Luu Anh Tuan, Shuai Zhang, Shuohang Wang, Siu Cheung Hui, Yi Tay","submitted_at":"2019-06-11T04:56:17Z","abstract_excerpt":"Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly ($75\\%$) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Qua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04393","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.04393","created_at":"2026-05-17T23:43:39.404335+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04393v1","created_at":"2026-05-17T23:43:39.404335+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04393","created_at":"2026-05-17T23:43:39.404335+00:00"},{"alias_kind":"pith_short_12","alias_value":"ALU7SZSEYVKG","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"ALU7SZSEYVKGXLJC","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"ALU7SZSE","created_at":"2026-05-18T12:33:12.712433+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/ALU7SZSEYVKGXLJCDR53XSFEBU","json":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU.json","graph_json":"https://pith.science/api/pith-number/ALU7SZSEYVKGXLJCDR53XSFEBU/graph.json","events_json":"https://pith.science/api/pith-number/ALU7SZSEYVKGXLJCDR53XSFEBU/events.json","paper":"https://pith.science/paper/ALU7SZSE"},"agent_actions":{"view_html":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU","download_json":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU.json","view_paper":"https://pith.science/paper/ALU7SZSE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04393&json=true","fetch_graph":"https://pith.science/api/pith-number/ALU7SZSEYVKGXLJCDR53XSFEBU/graph.json","fetch_events":"https://pith.science/api/pith-number/ALU7SZSEYVKGXLJCDR53XSFEBU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU/action/storage_attestation","attest_author":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU/action/author_attestation","sign_citation":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU/action/citation_signature","submit_replication":"https://pith.science/pith/ALU7SZSEYVKGXLJCDR53XSFEBU/action/replication_record"}},"created_at":"2026-05-17T23:43:39.404335+00:00","updated_at":"2026-05-17T23:43:39.404335+00:00"}