SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 555 papers

TitleStatusHype
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering—0
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension—0
Relation Module for Non-Answerable Predictions on Reading Comprehension—0
Relation Module for Non-answerable Prediction on Question Answering—0
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension—0
Rethinking Annotation: Can Language Learners Contribute?—0
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering—0
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension—0
Revisiting the Open-Domain Question Answering Pipeline—0
Robust Domain Adaptation for Machine Reading Comprehension—0
Robustly Optimized and Distilled Training for Natural Language Understanding—0
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
Show:102550
← PrevPage 12 of 23Next →

No leaderboard results yet.