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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 301–350 of 555 papers

TitleStatusHype
Machine Reading Comprehension with Enhanced Linguistic Verifiers—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
Coreference Reasoning in Machine Reading ComprehensionCode0
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
SG-Net: Syntax Guided Transformer for Language Representation—0
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension—0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
Semantics Altering Modifications for Evaluating Comprehension in Machine ReadingCode0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
Reference Knowledgeable Network for Machine Reading ComprehensionCode0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training—0
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension—0
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian—0
A Multilingual Reading Comprehension System for more than 100 Languages—0
Multi-choice Relational Reasoning for Machine Reading Comprehension—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
SQL Generation via Machine Reading ComprehensionCode0
Bi-directional CognitiveThinking Network for Machine Reading Comprehension—0
Robust Machine Reading Comprehension by Learning Soft labels—0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension—0
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension—0
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training—0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection—0
Unsupervised Explanation Generation for Machine Reading Comprehension—0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling—0
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading ComprehensionCode0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Answer Span Correction in Machine Reading Comprehension—0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test—0
Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It’s complicated!—0
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text—0
Event Extraction as Machine Reading Comprehension—0
Scene Restoring for Narrative Machine Reading Comprehension—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications—0
Improved Synthetic Training for Reading Comprehension—0
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension—0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension—0
Context Modeling with Evidence Filter for Multiple Choice Question Answering—0
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceCode0
ARES: A Reading Comprehension Ensembling Service—0
A Survey on Explainability in Machine Reading Comprehension—0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension—0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension—0
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension—0
No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension—0
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