{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:NZ65FIS6A25XZG5OKP2MCP6U57","short_pith_number":"pith:NZ65FIS6","canonical_record":{"source":{"id":"1510.07526","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-10-26T16:03:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0b7a005cea44233e3865266340542844ffaec9c4288b043d85e629e67e6777b9","abstract_canon_sha256":"5f8aefceaa4abbc9f26f826b2f47219f828a3c150198103657b00d4289e39210"},"schema_version":"1.0"},"canonical_sha256":"6e7dd2a25e06bb7c9bae53f4c13fd4efe2517c214df94ff65c296ecb2dd31185","source":{"kind":"arxiv","id":"1510.07526","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.07526","created_at":"2026-05-18T01:26:24Z"},{"alias_kind":"arxiv_version","alias_value":"1510.07526v3","created_at":"2026-05-18T01:26:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.07526","created_at":"2026-05-18T01:26:24Z"},{"alias_kind":"pith_short_12","alias_value":"NZ65FIS6A25X","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"NZ65FIS6A25XZG5O","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"NZ65FIS6","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:NZ65FIS6A25XZG5OKP2MCP6U57","target":"record","payload":{"canonical_record":{"source":{"id":"1510.07526","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-10-26T16:03:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0b7a005cea44233e3865266340542844ffaec9c4288b043d85e629e67e6777b9","abstract_canon_sha256":"5f8aefceaa4abbc9f26f826b2f47219f828a3c150198103657b00d4289e39210"},"schema_version":"1.0"},"canonical_sha256":"6e7dd2a25e06bb7c9bae53f4c13fd4efe2517c214df94ff65c296ecb2dd31185","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:24.897036Z","signature_b64":"6KsTkwNjySCMFakod7oEQMdvY/6R+909p9e62mZiYfcMGPaU8llepcmqIpmjMrrfhqTgPMod43ffjrMITrKZDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e7dd2a25e06bb7c9bae53f4c13fd4efe2517c214df94ff65c296ecb2dd31185","last_reissued_at":"2026-05-18T01:26:24.896614Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:24.896614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1510.07526","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:26:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i+V3f6kKk581qxmLEsueSM8yYi6RX9ej+7Tr/YnJNZj7AGnN0WjPO+GEviw/8GYdGPN2v7pslcgAm1LTWtE0Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:58:53.688381Z"},"content_sha256":"434cd5c18cb6e6910759ee859bc2782c97d61fb69b2877428cd67be7e3477166","schema_version":"1.0","event_id":"sha256:434cd5c18cb6e6910759ee859bc2782c97d61fb69b2877428cd67be7e3477166"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:NZ65FIS6A25XZG5OKP2MCP6U57","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Empirical Study on Deep Learning Models for Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bing Xiang, Bowen Zhou, Chung-Wei Hang, Wei Zhang, Yang Yu","submitted_at":"2015-10-26T16:03:27Z","abstract_excerpt":"In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation, Neural Turing Machine, and Memory Networks for a simulated QA data set. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.07526","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:26:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aq5k7wVwE7h2p5ic3TePe18lga+OGtelEy7TVhDob/MGr7Hv7NL6QRlbCixztxeYGBJshClkzovTxg8d4B7NDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:58:53.689086Z"},"content_sha256":"ee37dbcda69ef007bc76ae9ae71170aade95264ec64c9c0ee693140627ee3072","schema_version":"1.0","event_id":"sha256:ee37dbcda69ef007bc76ae9ae71170aade95264ec64c9c0ee693140627ee3072"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NZ65FIS6A25XZG5OKP2MCP6U57/bundle.json","state_url":"https://pith.science/pith/NZ65FIS6A25XZG5OKP2MCP6U57/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NZ65FIS6A25XZG5OKP2MCP6U57/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T23:58:53Z","links":{"resolver":"https://pith.science/pith/NZ65FIS6A25XZG5OKP2MCP6U57","bundle":"https://pith.science/pith/NZ65FIS6A25XZG5OKP2MCP6U57/bundle.json","state":"https://pith.science/pith/NZ65FIS6A25XZG5OKP2MCP6U57/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NZ65FIS6A25XZG5OKP2MCP6U57/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:NZ65FIS6A25XZG5OKP2MCP6U57","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5f8aefceaa4abbc9f26f826b2f47219f828a3c150198103657b00d4289e39210","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-10-26T16:03:27Z","title_canon_sha256":"0b7a005cea44233e3865266340542844ffaec9c4288b043d85e629e67e6777b9"},"schema_version":"1.0","source":{"id":"1510.07526","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.07526","created_at":"2026-05-18T01:26:24Z"},{"alias_kind":"arxiv_version","alias_value":"1510.07526v3","created_at":"2026-05-18T01:26:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.07526","created_at":"2026-05-18T01:26:24Z"},{"alias_kind":"pith_short_12","alias_value":"NZ65FIS6A25X","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"NZ65FIS6A25XZG5O","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"NZ65FIS6","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:ee37dbcda69ef007bc76ae9ae71170aade95264ec64c9c0ee693140627ee3072","target":"graph","created_at":"2026-05-18T01:26:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation, Neural Turing Machine, and Memory Networks for a simulated QA data set. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.","authors_text":"Bing Xiang, Bowen Zhou, Chung-Wei Hang, Wei Zhang, Yang Yu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-10-26T16:03:27Z","title":"Empirical Study on Deep Learning Models for Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.07526","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:434cd5c18cb6e6910759ee859bc2782c97d61fb69b2877428cd67be7e3477166","target":"record","created_at":"2026-05-18T01:26:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5f8aefceaa4abbc9f26f826b2f47219f828a3c150198103657b00d4289e39210","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-10-26T16:03:27Z","title_canon_sha256":"0b7a005cea44233e3865266340542844ffaec9c4288b043d85e629e67e6777b9"},"schema_version":"1.0","source":{"id":"1510.07526","kind":"arxiv","version":3}},"canonical_sha256":"6e7dd2a25e06bb7c9bae53f4c13fd4efe2517c214df94ff65c296ecb2dd31185","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e7dd2a25e06bb7c9bae53f4c13fd4efe2517c214df94ff65c296ecb2dd31185","first_computed_at":"2026-05-18T01:26:24.896614Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:26:24.896614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6KsTkwNjySCMFakod7oEQMdvY/6R+909p9e62mZiYfcMGPaU8llepcmqIpmjMrrfhqTgPMod43ffjrMITrKZDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:26:24.897036Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.07526","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:434cd5c18cb6e6910759ee859bc2782c97d61fb69b2877428cd67be7e3477166","sha256:ee37dbcda69ef007bc76ae9ae71170aade95264ec64c9c0ee693140627ee3072"],"state_sha256":"060418dc8426cf68dbf8615ce6b02acd47d0e14878bb5744aa0b1821ee9e1e73"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5WM7YUxbHzTbYUjHtq4I6+j05kmRpVDmrBUeeo5ZZGJksJy4wWDcp3Zcx2hz0oH7p32XHowX81HcNkEf6vzXBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T23:58:53.693307Z","bundle_sha256":"a88f9cdb6e714530e188c5bc340e3674a822aaf841780f482b1e06490a602412"}}