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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 426–450 of 555 papers

TitleStatusHype
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
Label Dependent Deep Variational Paraphrase Generation—0
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets—0
Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization—0
Improving Machine Reading Comprehension via Adversarial Training—0
Ask to Learn: A Study on Curiosity-driven Question Generation—0
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension—0
Dice Loss for Data-imbalanced NLP TasksCode0
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations—0
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations—0
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading ComprehensionCode0
Relation Module for Non-Answerable Predictions on Reading Comprehension—0
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network—0
Cross-Task Knowledge Transfer for Query-Based Text Summarization—0
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks—0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension—0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior—0
On Making Reading Comprehension More Comprehensive—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
Relation Module for Non-answerable Prediction on Question Answering—0
Why can't memory networks read effectively?—0
NumNet: Machine Reading Comprehension with Numerical ReasoningCode0
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on NovelsCode0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
MMM: Multi-stage Multi-task Learning for Multi-choice Reading ComprehensionCode0
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