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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 51–75 of 555 papers

TitleStatusHype
Dependency Parsing as MRC-based Span-Span PredictionCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
Multitask Pre-training of Modular Prompt for Chinese Few-Shot LearningCode1
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named EntitiesCode1
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading ComprehensionCode1
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
ReCO: A Large Scale Chinese Reading Comprehension Dataset on OpinionCode1
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
Retrospective Reader for Machine Reading ComprehensionCode1
RoR: Read-over-Read for Long Document Machine Reading ComprehensionCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
TREC CAsT 2019: The Conversational Assistance Track OverviewCode1
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