{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:H4RBU5Q3UR4J3F2BI3IZTZJX5F","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":"7dbb082a68604ee6f43f69535fd0ccc8acee33911ed2498a8039dc18b23d5d45","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2023-08-22T14:42:27Z","title_canon_sha256":"3ab08eaa0f559e0f8a6da5243ba0b6c4cf1aa7868db51a5ef0dfe2220ec241ea"},"schema_version":"1.0","source":{"id":"2308.11474","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.11474","created_at":"2026-07-05T06:43:35Z"},{"alias_kind":"arxiv_version","alias_value":"2308.11474v1","created_at":"2026-07-05T06:43:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.11474","created_at":"2026-07-05T06:43:35Z"},{"alias_kind":"pith_short_12","alias_value":"H4RBU5Q3UR4J","created_at":"2026-07-05T06:43:35Z"},{"alias_kind":"pith_short_16","alias_value":"H4RBU5Q3UR4J3F2B","created_at":"2026-07-05T06:43:35Z"},{"alias_kind":"pith_short_8","alias_value":"H4RBU5Q3","created_at":"2026-07-05T06:43:35Z"}],"graph_snapshots":[{"event_id":"sha256:cbe09fb7714ec191b97a37977fdd510b03599f4d8704063f9a2cdc1efe988408","target":"graph","created_at":"2026-07-05T06:43:35Z","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/2308.11474/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by ","authors_text":"Fan Yixing, Hongyu Shan, Jiafeng Guo, Keping Bi, Qishen Zhang, Xiaojie Sun, Xinyu Ma, Zhongyi Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2023-08-22T14:42:27Z","title":"Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.11474","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":"sha256:87ae6396bce724c7b6697a0f34e2bdf3e162d2b83fd2e66b5ec525ebd87eb058","target":"record","created_at":"2026-07-05T06:43:35Z","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":"7dbb082a68604ee6f43f69535fd0ccc8acee33911ed2498a8039dc18b23d5d45","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2023-08-22T14:42:27Z","title_canon_sha256":"3ab08eaa0f559e0f8a6da5243ba0b6c4cf1aa7868db51a5ef0dfe2220ec241ea"},"schema_version":"1.0","source":{"id":"2308.11474","kind":"arxiv","version":1}},"canonical_sha256":"3f221a761ba4789d974146d199e537e973a40079a6fda78bf990efb638f9e498","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f221a761ba4789d974146d199e537e973a40079a6fda78bf990efb638f9e498","first_computed_at":"2026-07-05T06:43:35.443032Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:43:35.443032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qg0tVu/WbEq7gU7CMk0zU9naZYNv8WGrcf5KSUSpTG4uNzWeYAaR010E/y8sPFIXIK+vFgjfPzPflTVGOlZ/Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:43:35.443521Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.11474","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87ae6396bce724c7b6697a0f34e2bdf3e162d2b83fd2e66b5ec525ebd87eb058","sha256:cbe09fb7714ec191b97a37977fdd510b03599f4d8704063f9a2cdc1efe988408"],"state_sha256":"e3773941d878b91219b1801f966d135d72582b4635881d2c1f454907f51181da"}