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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 276–300 of 555 papers

TitleStatusHype
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)—0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications—0
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction—0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification—0
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph—0
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization—0
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales—0
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task—0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings—0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
Does Structure Matter? Encoding Documents for Machine Reading Comprehension—0
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering—0
THG: Transformer with Hyperbolic Geometry—0
NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension—0
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models—0
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese—0
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction—0
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