{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:STHJBETDVJH3AHCMN3VAJQXI5G","short_pith_number":"pith:STHJBETD","schema_version":"1.0","canonical_sha256":"94ce909263aa4fb01c4c6eea04c2e8e9b48f102e75640d648517f86905663feb","source":{"kind":"arxiv","id":"2212.04070","version":1},"attestation_state":"computed","paper":{"title":"A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"(2) Assistant Professor in University of Isfahan, (3) Professor of Computer Engineering in University of Isfahan), Ahmad Baraani (3) ((1) Candidate student in University of Isfahan, Azade Mohammadi (1), Reza Ramezani (2)","submitted_at":"2022-12-08T04:42:59Z","abstract_excerpt":"Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC ev"},"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":"2212.04070","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-08T04:42:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ec996c3d1a456ee36986b977caa219f9461cc7014b37e043a7da29f022a9a0b6","abstract_canon_sha256":"a93847bc65e7d31f163d255df038e7e7e3c885dcd369653e3ba538a1e3186153"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:23:36.022431Z","signature_b64":"cTL1MZxMmh2cGdL7m7kECmBQNsv2ZpN5Dm6xx0IOc9fD4SH5XDK/gPILqJ+2vF0FKJ+oM9qgY82c/PhUd3H/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94ce909263aa4fb01c4c6eea04c2e8e9b48f102e75640d648517f86905663feb","last_reissued_at":"2026-07-05T05:23:36.022036Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:23:36.022036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"(2) Assistant Professor in University of Isfahan, (3) Professor of Computer Engineering in University of Isfahan), Ahmad Baraani (3) ((1) Candidate student in University of Isfahan, Azade Mohammadi (1), Reza Ramezani (2)","submitted_at":"2022-12-08T04:42:59Z","abstract_excerpt":"Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC ev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04070","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/2212.04070/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":"2212.04070","created_at":"2026-07-05T05:23:36.022091+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.04070v1","created_at":"2026-07-05T05:23:36.022091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04070","created_at":"2026-07-05T05:23:36.022091+00:00"},{"alias_kind":"pith_short_12","alias_value":"STHJBETDVJH3","created_at":"2026-07-05T05:23:36.022091+00:00"},{"alias_kind":"pith_short_16","alias_value":"STHJBETDVJH3AHCM","created_at":"2026-07-05T05:23:36.022091+00:00"},{"alias_kind":"pith_short_8","alias_value":"STHJBETD","created_at":"2026-07-05T05:23:36.022091+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.08270","citing_title":"Interpretable Physics Reasoning and Performance Taxonomy in Vision-Language Models","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G","json":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G.json","graph_json":"https://pith.science/api/pith-number/STHJBETDVJH3AHCMN3VAJQXI5G/graph.json","events_json":"https://pith.science/api/pith-number/STHJBETDVJH3AHCMN3VAJQXI5G/events.json","paper":"https://pith.science/paper/STHJBETD"},"agent_actions":{"view_html":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G","download_json":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G.json","view_paper":"https://pith.science/paper/STHJBETD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.04070&json=true","fetch_graph":"https://pith.science/api/pith-number/STHJBETDVJH3AHCMN3VAJQXI5G/graph.json","fetch_events":"https://pith.science/api/pith-number/STHJBETDVJH3AHCMN3VAJQXI5G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G/action/storage_attestation","attest_author":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G/action/author_attestation","sign_citation":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G/action/citation_signature","submit_replication":"https://pith.science/pith/STHJBETDVJH3AHCMN3VAJQXI5G/action/replication_record"}},"created_at":"2026-07-05T05:23:36.022091+00:00","updated_at":"2026-07-05T05:23:36.022091+00:00"}