{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OGYQXTLYCRSCITPTXQHDZZFCSS","short_pith_number":"pith:OGYQXTLY","schema_version":"1.0","canonical_sha256":"71b10bcd781464244df3bc0e3ce4a2948ec09d68100d965c581380a300f828f7","source":{"kind":"arxiv","id":"2208.08797","version":2},"attestation_state":"computed","paper":{"title":"Exploiting Sentiment and Common Sense for Zero-shot Stance Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Stan Z Li, Yuefeng Shi, Yue Zhang, Yun Luo, Zihan Liu","submitted_at":"2022-08-18T12:27:24Z","abstract_excerpt":"The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-s"},"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":"2208.08797","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-18T12:27:24Z","cross_cats_sorted":[],"title_canon_sha256":"d863ae6e2cc3f826d325b719e06406777553f9d4dcffa57bd98a567393787654","abstract_canon_sha256":"d84a27837901031aab3bf1ebfbae609527cccb8b068a8a8a50a4569452ee7de4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:03:39.065671Z","signature_b64":"liBTFJ91EqCIvhyzu6ZL6JbetFDbIhwy95P1uo5V/LR7wvGGwtVxNviIUkn5a/ZqATcKgVTsqVHyTq2fh/NVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71b10bcd781464244df3bc0e3ce4a2948ec09d68100d965c581380a300f828f7","last_reissued_at":"2026-07-05T05:03:39.065181Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:03:39.065181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploiting Sentiment and Common Sense for Zero-shot Stance Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Stan Z Li, Yuefeng Shi, Yue Zhang, Yun Luo, Zihan Liu","submitted_at":"2022-08-18T12:27:24Z","abstract_excerpt":"The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.08797","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.08797/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":"2208.08797","created_at":"2026-07-05T05:03:39.065235+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.08797v2","created_at":"2026-07-05T05:03:39.065235+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.08797","created_at":"2026-07-05T05:03:39.065235+00:00"},{"alias_kind":"pith_short_12","alias_value":"OGYQXTLYCRSC","created_at":"2026-07-05T05:03:39.065235+00:00"},{"alias_kind":"pith_short_16","alias_value":"OGYQXTLYCRSCITPT","created_at":"2026-07-05T05:03:39.065235+00:00"},{"alias_kind":"pith_short_8","alias_value":"OGYQXTLY","created_at":"2026-07-05T05:03:39.065235+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/OGYQXTLYCRSCITPTXQHDZZFCSS","json":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS.json","graph_json":"https://pith.science/api/pith-number/OGYQXTLYCRSCITPTXQHDZZFCSS/graph.json","events_json":"https://pith.science/api/pith-number/OGYQXTLYCRSCITPTXQHDZZFCSS/events.json","paper":"https://pith.science/paper/OGYQXTLY"},"agent_actions":{"view_html":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS","download_json":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS.json","view_paper":"https://pith.science/paper/OGYQXTLY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.08797&json=true","fetch_graph":"https://pith.science/api/pith-number/OGYQXTLYCRSCITPTXQHDZZFCSS/graph.json","fetch_events":"https://pith.science/api/pith-number/OGYQXTLYCRSCITPTXQHDZZFCSS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS/action/storage_attestation","attest_author":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS/action/author_attestation","sign_citation":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS/action/citation_signature","submit_replication":"https://pith.science/pith/OGYQXTLYCRSCITPTXQHDZZFCSS/action/replication_record"}},"created_at":"2026-07-05T05:03:39.065235+00:00","updated_at":"2026-07-05T05:03:39.065235+00:00"}