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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 401–425 of 555 papers

TitleStatusHype
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering—0
Ask to Learn: A Study on Curiosity-driven Question Generation—0
Aspect-based Sentiment Analysis as Machine Reading Comprehension—0
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets—0
A Study of the Tasks and Models in Machine Reading Comprehension—0
A Study on Contextualized Language Modeling for Machine Reading Comprehension—0
A Survey on Explainability in Machine Reading Comprehension—0
A Survey on Machine Reading Comprehension Systems—0
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets—0
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension—0
A Survey on Neural Machine Reading Comprehension—0
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes—0
Attention-Guided Answer Distillation for Machine Reading Comprehension—0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
A Unified Abstractive Model for Generating Question-Answer Pairs—0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model—0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension—0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension—0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications—0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change—0
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension—0
Bi-directional CognitiveThinking Network for Machine Reading Comprehension—0
Biomedical Question Answering: A Survey of Approaches and Challenges—0
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations—0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension—0
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