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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 301–325 of 555 papers

TitleStatusHype
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
To Test Machine Comprehension, Start by Defining Comprehension—0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Towards Confident Machine Reading Comprehension—0
Towards Inference-Oriented Reading Comprehension: ParallelQA—0
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text—0
Towards Robust Neural Retrieval Models with Synthetic Pre-Training—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
Trigger-free Event Detection via Derangement Reading Comprehension—0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension—0
Uncertainty-Based Adaptive Learning for Reading Comprehension—0
Understanding Attention in Machine Reading Comprehension—0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data—0
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