{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:LNBWKVJHYWMYOEALKRN42XSUL3","short_pith_number":"pith:LNBWKVJH","canonical_record":{"source":{"id":"2109.03009","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2021-09-07T11:48:23Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"8af2a7faa788e36acac88ebfda0625b5d6c7693ea46d87c5697faaa3c382cf71","abstract_canon_sha256":"334f7159f37254f89163b68f2bd5520268f60ff5557f4afd17f88fcb2e67bac4"},"schema_version":"1.0"},"canonical_sha256":"5b43655527c59987100b545bcd5e545ecfa2ad9bb4b93fbd062a5791a22dfd9f","source":{"kind":"arxiv","id":"2109.03009","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.03009","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"arxiv_version","alias_value":"2109.03009v1","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.03009","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"pith_short_12","alias_value":"LNBWKVJHYWMY","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"pith_short_16","alias_value":"LNBWKVJHYWMYOEAL","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"pith_short_8","alias_value":"LNBWKVJH","created_at":"2026-07-05T03:12:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:LNBWKVJHYWMYOEALKRN42XSUL3","target":"record","payload":{"canonical_record":{"source":{"id":"2109.03009","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2021-09-07T11:48:23Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"8af2a7faa788e36acac88ebfda0625b5d6c7693ea46d87c5697faaa3c382cf71","abstract_canon_sha256":"334f7159f37254f89163b68f2bd5520268f60ff5557f4afd17f88fcb2e67bac4"},"schema_version":"1.0"},"canonical_sha256":"5b43655527c59987100b545bcd5e545ecfa2ad9bb4b93fbd062a5791a22dfd9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:22.672543Z","signature_b64":"/jPsBLWb9mrrWRiRqD/Zl2/3Ln8isAOYammLP+YlBCeMSXWjn/dQaLIIfEmOhxO81eBqS5zDfT8/JD42OSmYBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b43655527c59987100b545bcd5e545ecfa2ad9bb4b93fbd062a5791a22dfd9f","last_reissued_at":"2026-07-05T03:12:22.672135Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:22.672135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.03009","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":"2026-07-05T03:12:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lfkmbrc28XYjCawsoaaMXV94YdTzQokkp12pSgt/NWEJ4XdxwsheHJCAJsU7pAYSTQ/uGncaEvgRJOVi2dX/AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:13:43.243278Z"},"content_sha256":"2d1bc6aeb388daa47c69d4443afbf98b49bb0ce1826c209da5ef33c9e3c74168","schema_version":"1.0","event_id":"sha256:2d1bc6aeb388daa47c69d4443afbf98b49bb0ce1826c209da5ef33c9e3c74168"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:LNBWKVJHYWMYOEALKRN42XSUL3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sequential Attention Module for Natural Language Processing","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Haiqin Yang, Jian Ma, Lianxin Jiang, Mengyuan Zhou, Yang Mo","submitted_at":"2021-09-07T11:48:23Z","abstract_excerpt":"Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this paper, we target at how to further improve the token representations on the language models. We, therefore, propose a simple yet effective plug-and-play module, Sequential Attention Module (SAM), on the token embeddings learned from a pre-trained language model. Our proposed SAM consists of two main attention modules deployed sequentially: Feature-wise Attention Module (FAM) and Token-wise Attention Module (TAM). More"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.03009","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/2109.03009/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":"2026-07-05T03:12:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KFrydmMLc8BhcWsywHVorNXLXQULkWDtdo5f5W23QSO9ZsrNcXDtrUx+uJG0bmCu0abFhnLb2/jyWNLnT1NzCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:13:43.243663Z"},"content_sha256":"9af522f2d046b8bf3c4fd74b67352a2bcfb2a72ce22400247cdbdd25a30b50d7","schema_version":"1.0","event_id":"sha256:9af522f2d046b8bf3c4fd74b67352a2bcfb2a72ce22400247cdbdd25a30b50d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LNBWKVJHYWMYOEALKRN42XSUL3/bundle.json","state_url":"https://pith.science/pith/LNBWKVJHYWMYOEALKRN42XSUL3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LNBWKVJHYWMYOEALKRN42XSUL3/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":"2026-07-07T14:13:43Z","links":{"resolver":"https://pith.science/pith/LNBWKVJHYWMYOEALKRN42XSUL3","bundle":"https://pith.science/pith/LNBWKVJHYWMYOEALKRN42XSUL3/bundle.json","state":"https://pith.science/pith/LNBWKVJHYWMYOEALKRN42XSUL3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LNBWKVJHYWMYOEALKRN42XSUL3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:LNBWKVJHYWMYOEALKRN42XSUL3","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":"334f7159f37254f89163b68f2bd5520268f60ff5557f4afd17f88fcb2e67bac4","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2021-09-07T11:48:23Z","title_canon_sha256":"8af2a7faa788e36acac88ebfda0625b5d6c7693ea46d87c5697faaa3c382cf71"},"schema_version":"1.0","source":{"id":"2109.03009","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.03009","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"arxiv_version","alias_value":"2109.03009v1","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.03009","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"pith_short_12","alias_value":"LNBWKVJHYWMY","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"pith_short_16","alias_value":"LNBWKVJHYWMYOEAL","created_at":"2026-07-05T03:12:22Z"},{"alias_kind":"pith_short_8","alias_value":"LNBWKVJH","created_at":"2026-07-05T03:12:22Z"}],"graph_snapshots":[{"event_id":"sha256:9af522f2d046b8bf3c4fd74b67352a2bcfb2a72ce22400247cdbdd25a30b50d7","target":"graph","created_at":"2026-07-05T03:12:22Z","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/2109.03009/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this paper, we target at how to further improve the token representations on the language models. We, therefore, propose a simple yet effective plug-and-play module, Sequential Attention Module (SAM), on the token embeddings learned from a pre-trained language model. Our proposed SAM consists of two main attention modules deployed sequentially: Feature-wise Attention Module (FAM) and Token-wise Attention Module (TAM). More","authors_text":"Haiqin Yang, Jian Ma, Lianxin Jiang, Mengyuan Zhou, Yang Mo","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2021-09-07T11:48:23Z","title":"Sequential Attention Module for Natural Language Processing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.03009","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":"sha256:2d1bc6aeb388daa47c69d4443afbf98b49bb0ce1826c209da5ef33c9e3c74168","target":"record","created_at":"2026-07-05T03:12:22Z","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":"334f7159f37254f89163b68f2bd5520268f60ff5557f4afd17f88fcb2e67bac4","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2021-09-07T11:48:23Z","title_canon_sha256":"8af2a7faa788e36acac88ebfda0625b5d6c7693ea46d87c5697faaa3c382cf71"},"schema_version":"1.0","source":{"id":"2109.03009","kind":"arxiv","version":1}},"canonical_sha256":"5b43655527c59987100b545bcd5e545ecfa2ad9bb4b93fbd062a5791a22dfd9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b43655527c59987100b545bcd5e545ecfa2ad9bb4b93fbd062a5791a22dfd9f","first_computed_at":"2026-07-05T03:12:22.672135Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:12:22.672135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/jPsBLWb9mrrWRiRqD/Zl2/3Ln8isAOYammLP+YlBCeMSXWjn/dQaLIIfEmOhxO81eBqS5zDfT8/JD42OSmYBA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:12:22.672543Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.03009","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d1bc6aeb388daa47c69d4443afbf98b49bb0ce1826c209da5ef33c9e3c74168","sha256:9af522f2d046b8bf3c4fd74b67352a2bcfb2a72ce22400247cdbdd25a30b50d7"],"state_sha256":"f947d26775ffbb64b7660676f9d9fbfd9136e7d8bfb24a70bcc60ba084ff7f9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+OQdhjb5lDBlq11ZC9RATEKEnNM/2GHxsc0zf2ygfmG9nlmRWxKLaZPENt72a7D5iUaPPPbA67z1qVavvjEtAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:13:43.245595Z","bundle_sha256":"c86c9dccf2df80a799bf8263c116c8ad52f98833e735cdf5395b50c3b143ac4d"}}