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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 201–225 of 555 papers

TitleStatusHype
English Machine Reading Comprehension Datasets: A SurveyCode0
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
ARES: A Reading Comprehension Ensembling Service—0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
A Graph Fusion Approach to Cross-Lingual Machine Reading Comprehension—0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension—0
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension—0
A Constituent-Centric Neural Architecture for Reading Comprehension—0
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
CFO: A Framework for Building Production NLP Systems—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension—0
CALOR-QUEST : un corpus d'entra\^ et d'\'evaluation pour la compr\'ehension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)—0
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations—0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
A Frame-based Sentence Representation for Machine Reading Comprehension—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches—0
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