{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:35MASA6344QNFXRYIUKYWCXD52","short_pith_number":"pith:35MASA63","schema_version":"1.0","canonical_sha256":"df580903dbe720d2de3845158b0ae3eea77ef276f9b6d376ef8487d6c114f087","source":{"kind":"arxiv","id":"2006.03773","version":1},"attestation_state":"computed","paper":{"title":"Challenges and Thrills of Legal Arguments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Aditya Swaroop Attawar, Anurag Pallaprolu, Radha Vaidya","submitted_at":"2020-06-06T03:43:15Z","abstract_excerpt":"State-of-the-art attention based models, mostly centered around the transformer architecture, solve the problem of sequence-to-sequence translation using the so-called scaled dot-product attention. While this technique is highly effective for estimating inter-token attention, it does not answer the question of inter-sequence attention when we deal with conversation-like scenarios. We propose an extension, HumBERT, that attempts to perform continuous contextual argument generation using locally trained transformers."},"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":"2006.03773","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-06T03:43:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c6401a37d7f994fa46fc7f828091fdbaac40498996ea5eefa1c5622bafed6d18","abstract_canon_sha256":"9ad64d9c742d5d9a18c477e82dbcae817dffe506e398532843680d0093a37fd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:32.937830Z","signature_b64":"3FXMbC87VijqlfnmgX/6zvC79ZJazRLGCdQXb0j1k08D39b7Odq6zMTIZb3yeg34K0fMJTXsF9whhWkNUWuMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df580903dbe720d2de3845158b0ae3eea77ef276f9b6d376ef8487d6c114f087","last_reissued_at":"2026-07-05T01:08:32.937453Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:32.937453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Challenges and Thrills of Legal Arguments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Aditya Swaroop Attawar, Anurag Pallaprolu, Radha Vaidya","submitted_at":"2020-06-06T03:43:15Z","abstract_excerpt":"State-of-the-art attention based models, mostly centered around the transformer architecture, solve the problem of sequence-to-sequence translation using the so-called scaled dot-product attention. While this technique is highly effective for estimating inter-token attention, it does not answer the question of inter-sequence attention when we deal with conversation-like scenarios. We propose an extension, HumBERT, that attempts to perform continuous contextual argument generation using locally trained transformers."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.03773","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/2006.03773/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":"2006.03773","created_at":"2026-07-05T01:08:32.937514+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.03773v1","created_at":"2026-07-05T01:08:32.937514+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.03773","created_at":"2026-07-05T01:08:32.937514+00:00"},{"alias_kind":"pith_short_12","alias_value":"35MASA6344QN","created_at":"2026-07-05T01:08:32.937514+00:00"},{"alias_kind":"pith_short_16","alias_value":"35MASA6344QNFXRY","created_at":"2026-07-05T01:08:32.937514+00:00"},{"alias_kind":"pith_short_8","alias_value":"35MASA63","created_at":"2026-07-05T01:08:32.937514+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/35MASA6344QNFXRYIUKYWCXD52","json":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52.json","graph_json":"https://pith.science/api/pith-number/35MASA6344QNFXRYIUKYWCXD52/graph.json","events_json":"https://pith.science/api/pith-number/35MASA6344QNFXRYIUKYWCXD52/events.json","paper":"https://pith.science/paper/35MASA63"},"agent_actions":{"view_html":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52","download_json":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52.json","view_paper":"https://pith.science/paper/35MASA63","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.03773&json=true","fetch_graph":"https://pith.science/api/pith-number/35MASA6344QNFXRYIUKYWCXD52/graph.json","fetch_events":"https://pith.science/api/pith-number/35MASA6344QNFXRYIUKYWCXD52/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52/action/storage_attestation","attest_author":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52/action/author_attestation","sign_citation":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52/action/citation_signature","submit_replication":"https://pith.science/pith/35MASA6344QNFXRYIUKYWCXD52/action/replication_record"}},"created_at":"2026-07-05T01:08:32.937514+00:00","updated_at":"2026-07-05T01:08:32.937514+00:00"}