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Machine Reading Comprehension

Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 21–30 of 555 papers

TitleStatusHype
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering—0
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—0
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
From Multiple-Choice to Extractive QA: A Case Study for English and ArabicCode0
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering—0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information—0
Interpreting Themes from Educational StoriesCode0
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0—0
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
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