{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DMNTGEDHDKDZ6ZPBUT633QY5VJ","short_pith_number":"pith:DMNTGEDH","schema_version":"1.0","canonical_sha256":"1b1b3310671a879f65e1a4fdbdc31daa5b82e67fa7ffc0dc64ec4e82bcba318f","source":{"kind":"arxiv","id":"2302.09300","version":1},"attestation_state":"computed","paper":{"title":"Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ante Wang, Dong Yu, Haitao Mi, Jinsong Su, Linfeng Song, Longyue Wang, Qi Liu, Zhaopeng Tu","submitted_at":"2023-02-16T01:58:10Z","abstract_excerpt":"Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of previous work assumes that the relevant knowledge is given as input or retrieved from a static pool of knowledge. However, this assumption violates the real-world situation, where knowledge is continually updated and a chatbot has to dynamically retrieve useful knowledge. We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation. As the core module, a quer"},"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":"2302.09300","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-16T01:58:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1f7e032593d20d7b632f618132ed8b3e32db42ded3d62caa6e6c06a7b889ffb9","abstract_canon_sha256":"f655dedd3544c388d019cbbefb73287dbb59084cc413139f9208bc9228c192a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:43:23.813457Z","signature_b64":"DtqnrVfe5ulE5yFGPlCEKUieQ9vaj3SVNrQxIktzpA1WNswXx/ylZAaFW5JTChSbt8LEgcrGXV6dAkOKqf89DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b1b3310671a879f65e1a4fdbdc31daa5b82e67fa7ffc0dc64ec4e82bcba318f","last_reissued_at":"2026-07-05T05:43:23.813057Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:43:23.813057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ante Wang, Dong Yu, Haitao Mi, Jinsong Su, Linfeng Song, Longyue Wang, Qi Liu, Zhaopeng Tu","submitted_at":"2023-02-16T01:58:10Z","abstract_excerpt":"Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of previous work assumes that the relevant knowledge is given as input or retrieved from a static pool of knowledge. However, this assumption violates the real-world situation, where knowledge is continually updated and a chatbot has to dynamically retrieve useful knowledge. We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation. As the core module, a quer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.09300","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/2302.09300/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2302.09300","created_at":"2026-07-05T05:43:23.813113+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.09300v1","created_at":"2026-07-05T05:43:23.813113+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.09300","created_at":"2026-07-05T05:43:23.813113+00:00"},{"alias_kind":"pith_short_12","alias_value":"DMNTGEDHDKDZ","created_at":"2026-07-05T05:43:23.813113+00:00"},{"alias_kind":"pith_short_16","alias_value":"DMNTGEDHDKDZ6ZPB","created_at":"2026-07-05T05:43:23.813113+00:00"},{"alias_kind":"pith_short_8","alias_value":"DMNTGEDH","created_at":"2026-07-05T05:43:23.813113+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/DMNTGEDHDKDZ6ZPBUT633QY5VJ","json":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ.json","graph_json":"https://pith.science/api/pith-number/DMNTGEDHDKDZ6ZPBUT633QY5VJ/graph.json","events_json":"https://pith.science/api/pith-number/DMNTGEDHDKDZ6ZPBUT633QY5VJ/events.json","paper":"https://pith.science/paper/DMNTGEDH"},"agent_actions":{"view_html":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ","download_json":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ.json","view_paper":"https://pith.science/paper/DMNTGEDH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.09300&json=true","fetch_graph":"https://pith.science/api/pith-number/DMNTGEDHDKDZ6ZPBUT633QY5VJ/graph.json","fetch_events":"https://pith.science/api/pith-number/DMNTGEDHDKDZ6ZPBUT633QY5VJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ/action/storage_attestation","attest_author":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ/action/author_attestation","sign_citation":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ/action/citation_signature","submit_replication":"https://pith.science/pith/DMNTGEDHDKDZ6ZPBUT633QY5VJ/action/replication_record"}},"created_at":"2026-07-05T05:43:23.813113+00:00","updated_at":"2026-07-05T05:43:23.813113+00:00"}