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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 251–300 of 555 papers

TitleStatusHype
On Making Reading Comprehension More Comprehensive—0
On the Multi-Property Extraction and Beyond—0
On the Robustness of Reading Comprehension Models to Entity Renaming—0
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data—0
OPERA:Operation-Pivoted Discrete Reasoning over Text—0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension—0
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales—0
Pay Attention to Real World Perturbations! Natural Robustness Evaluation in Machine Reading Comprehension—0
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering—0
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks—0
Benchmarking Machine Reading Comprehension: A Psychological Perspective—0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words—0
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents—0
QASE Enhanced PLMs: Improved Control in Text Generation for MRC—0
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications—0
Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It’s complicated!—0
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering—0
Query-Based Named Entity Recognition—0
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction—0
Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an—0
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian—0
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking—0
Read + Verify: Machine Reading Comprehension with Unanswerable Questions—0
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
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
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