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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
Robust Machine Reading Comprehension by Learning Soft labels—0
Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization—0
Scene Restoring for Narrative Machine Reading Comprehension—0
SciMRC: Multi-perspective Scientific Machine Reading Comprehension—0
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension—0
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data—0
Semantics-Aware Inferential Network for Natural Language Understanding—0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management—0
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
SG-Net: Syntax Guided Transformer for Language Representation—0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
SkillQG: Learning to Generate Question for Reading Comprehension Assessment—0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension—0
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0—0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Systematic Error Analysis of the Stanford Question Answering Dataset—0
Teach model to answer questions after comprehending the document—0
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0—0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
THG: Transformer with Hyperbolic Geometry—0
Ti-Reader: 基于注意力机制的藏文机器阅读理解端到端网络模型(Ti-Reader: An End-to-End Network Model Based on Attention Mechanisms for Tibetan Machine Reading Comprehension)—0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning—0
Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain—0
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions—0
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