{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PKA3PRD3PYPYSQRRMKQ5IFOOHH","short_pith_number":"pith:PKA3PRD3","schema_version":"1.0","canonical_sha256":"7a81b7c47b7e1f89423162a1d415ce39fb7b301355e9aab3b2c844a3594c2c3e","source":{"kind":"arxiv","id":"1801.06792","version":1},"attestation_state":"computed","paper":{"title":"Attentive Recurrent Tensor Model for Community Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Balasubramanian Raman, Gaurav Bhatt, Shivam Sharma","submitted_at":"2018-01-21T09:01:46Z","abstract_excerpt":"A major challenge to the problem of community question answering is the lexical and semantic gap between the sentence representations. Some solutions to minimize this gap includes the introduction of extra parameters to deep models or augmenting the external handcrafted features. In this paper, we propose a novel attentive recurrent tensor network for solving the lexical and semantic gap in community question answering. We introduce token-level and phrase-level attention strategy that maps input sequences to the output using trainable parameters. Further, we use the tensor parameters to introd"},"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":"1801.06792","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-21T09:01:46Z","cross_cats_sorted":[],"title_canon_sha256":"3c6883fe3d94be69e856287fbee46ae51a4f06f1ba6d4b8298fd31c6221071e2","abstract_canon_sha256":"f1766b896dd5e6a11438b72c5afca06f9c15e3bcddaeaa7c49466e7c7ce1a449"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:27.727190Z","signature_b64":"KPDJhW9de7BKj9DTEhoWkcNIjEZmNIBSG3zQ4KfqOn+D7tBDCgjspJebVN31Kfpj4sBMY4qBvvykP7fFTjsgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a81b7c47b7e1f89423162a1d415ce39fb7b301355e9aab3b2c844a3594c2c3e","last_reissued_at":"2026-05-18T00:25:27.726475Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:27.726475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Attentive Recurrent Tensor Model for Community Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Balasubramanian Raman, Gaurav Bhatt, Shivam Sharma","submitted_at":"2018-01-21T09:01:46Z","abstract_excerpt":"A major challenge to the problem of community question answering is the lexical and semantic gap between the sentence representations. Some solutions to minimize this gap includes the introduction of extra parameters to deep models or augmenting the external handcrafted features. In this paper, we propose a novel attentive recurrent tensor network for solving the lexical and semantic gap in community question answering. We introduce token-level and phrase-level attention strategy that maps input sequences to the output using trainable parameters. Further, we use the tensor parameters to introd"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06792","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":""},"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":"1801.06792","created_at":"2026-05-18T00:25:27.726592+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.06792v1","created_at":"2026-05-18T00:25:27.726592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06792","created_at":"2026-05-18T00:25:27.726592+00:00"},{"alias_kind":"pith_short_12","alias_value":"PKA3PRD3PYPY","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PKA3PRD3PYPYSQRR","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PKA3PRD3","created_at":"2026-05-18T12:32:46.962924+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/PKA3PRD3PYPYSQRRMKQ5IFOOHH","json":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH.json","graph_json":"https://pith.science/api/pith-number/PKA3PRD3PYPYSQRRMKQ5IFOOHH/graph.json","events_json":"https://pith.science/api/pith-number/PKA3PRD3PYPYSQRRMKQ5IFOOHH/events.json","paper":"https://pith.science/paper/PKA3PRD3"},"agent_actions":{"view_html":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH","download_json":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH.json","view_paper":"https://pith.science/paper/PKA3PRD3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.06792&json=true","fetch_graph":"https://pith.science/api/pith-number/PKA3PRD3PYPYSQRRMKQ5IFOOHH/graph.json","fetch_events":"https://pith.science/api/pith-number/PKA3PRD3PYPYSQRRMKQ5IFOOHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH/action/storage_attestation","attest_author":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH/action/author_attestation","sign_citation":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH/action/citation_signature","submit_replication":"https://pith.science/pith/PKA3PRD3PYPYSQRRMKQ5IFOOHH/action/replication_record"}},"created_at":"2026-05-18T00:25:27.726592+00:00","updated_at":"2026-05-18T00:25:27.726592+00:00"}