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Machine Reading Comprehension/Evidence
Method evidence record

Machine Reading Comprehension

Machine reading comprehension (MRC), popularised by the SQuAD benchmark of Rajpurkar, Zhang, Lopyrev and Liang (2016), is a natural-language-processing task in which a model reads a given passage and answers multiple-choice or open-ended questions about it. It turns a passage plus a question into a machine-generated answer, supporting information retrieval, educational technology, and querying research databases.

Sources recorded, not reviewed

Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Neural Machine Reading Comprehension (MRC)
Taxonomic method record · process-pipeline / text-mining
  • Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392. · DOI 10.18653/v1/D16-1264
  • Yang, Z. et al. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. EMNLP. · DOI 10.18653/v1/D18-1259
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

No curated claims yet

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyDomain Adaptationmachine-suggested · Relational suggestion, not evidence.Same method familySentiment Analysismachine-suggested · Relational suggestion, not evidence.Same method familyText Classificationmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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