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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 351–375 of 555 papers

TitleStatusHype
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation—0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection—0
FQuAD: French Question Answering Dataset—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents—0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions—0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—0
Graph Sequential Network for Reasoning over Sequences—0
Have You Seen That Number? Investigating Extrapolation in Question Answering Models—0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension—0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
Explicit Contextual Semantics for Text Comprehension—0
Improved Synthetic Training for Reading Comprehension—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Improving Cross-Lingual Reading Comprehension with Self-Training—0
Improving Machine Reading Comprehension via Adversarial Training—0
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
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