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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 251–275 of 555 papers

TitleStatusHype
Graph Sequential Network for Reasoning over Sequences—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Answer Span Correction in Machine Reading Comprehension—0
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Answer Generation through Unified Memories over Multiple Passages—0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations—0
Answer-focused and Position-aware Neural Question Generation—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension—0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training—0
Biomedical Question Answering: A Survey of Approaches and Challenges—0
An MRC Framework for Semantic Role Labeling—0
Adversarial reading networks for machine comprehension—0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information—0
Effective Character-augmented Word Embedding for Machine Reading Comprehension—0
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