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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
An MRC Framework for Semantic Role Labeling—0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—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
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension—0
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets—0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Answer Generation through Unified Memories over Multiple Passages—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
Event Detection via Derangement Reading Comprehension—0
Event Extraction as Machine Reading Comprehension—0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
A Frame-based Sentence Representation for Machine Reading Comprehension—0
Feeding What You Need by Understanding What You Learned—0
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