{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VBFQJWM4U6NCSMLRTAGMMBZQVD","short_pith_number":"pith:VBFQJWM4","schema_version":"1.0","canonical_sha256":"a84b04d99ca79a293171980cc60730a8d9efb52b86404b9931532bebd3cc9878","source":{"kind":"arxiv","id":"1907.08971","version":2},"attestation_state":"computed","paper":{"title":"Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Eyal Shnarch, Guy Moshkowich, Lena Dankin, Leshem Choshen, Martin Gleize, Noam Slonim, Ranit Aharonov","submitted_at":"2019-07-21T13:05:45Z","abstract_excerpt":"With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing al"},"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":"1907.08971","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-21T13:05:45Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"b075516a55e9029ef4c3898069f0680fe442aa9cc622cffaf0234fe18801f118","abstract_canon_sha256":"fbaa972356aedb3864bd3d034c728e6897f87242fd4fbc30fd7c36ccbab795a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:53.188079Z","signature_b64":"F7KqgQERjt0Ew4W0lBLXtoutXbLccmtbL+O4UoQ1xltELbDpPxB1lbovVO4mzXCTAhTcQObgoQmhr58u8cQlDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a84b04d99ca79a293171980cc60730a8d9efb52b86404b9931532bebd3cc9878","last_reissued_at":"2026-05-17T23:39:53.187545Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:53.187545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Eyal Shnarch, Guy Moshkowich, Lena Dankin, Leshem Choshen, Martin Gleize, Noam Slonim, Ranit Aharonov","submitted_at":"2019-07-21T13:05:45Z","abstract_excerpt":"With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.08971","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":""},"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":"1907.08971","created_at":"2026-05-17T23:39:53.187643+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.08971v2","created_at":"2026-05-17T23:39:53.187643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.08971","created_at":"2026-05-17T23:39:53.187643+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBFQJWM4U6NC","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBFQJWM4U6NCSMLR","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBFQJWM4","created_at":"2026-05-18T12:33:30.264802+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/VBFQJWM4U6NCSMLRTAGMMBZQVD","json":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD.json","graph_json":"https://pith.science/api/pith-number/VBFQJWM4U6NCSMLRTAGMMBZQVD/graph.json","events_json":"https://pith.science/api/pith-number/VBFQJWM4U6NCSMLRTAGMMBZQVD/events.json","paper":"https://pith.science/paper/VBFQJWM4"},"agent_actions":{"view_html":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD","download_json":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD.json","view_paper":"https://pith.science/paper/VBFQJWM4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.08971&json=true","fetch_graph":"https://pith.science/api/pith-number/VBFQJWM4U6NCSMLRTAGMMBZQVD/graph.json","fetch_events":"https://pith.science/api/pith-number/VBFQJWM4U6NCSMLRTAGMMBZQVD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD/action/storage_attestation","attest_author":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD/action/author_attestation","sign_citation":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD/action/citation_signature","submit_replication":"https://pith.science/pith/VBFQJWM4U6NCSMLRTAGMMBZQVD/action/replication_record"}},"created_at":"2026-05-17T23:39:53.187643+00:00","updated_at":"2026-05-17T23:39:53.187643+00:00"}