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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 526–550 of 555 papers

TitleStatusHype
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User ManualsCode0
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented GraphsCode0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and ResourcesCode0
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading ComprehensionCode0
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading ComprehensionCode0
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorCode0
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
Semantics Altering Modifications for Evaluating Comprehension in Machine ReadingCode0
From Multiple-Choice to Extractive QA: A Case Study for English and ArabicCode0
Semantics-aware BERT for Language UnderstandingCode0
Towards Efficient Methods in Medical Question Answering using Knowledge Graph EmbeddingsCode0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
ZeQR: Zero-shot Query Reformulation for Conversational SearchCode0
Lite Unified Modeling for Discriminative Reading ComprehensionCode0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
A Span-Extraction Dataset for Chinese Machine Reading ComprehensionCode0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
Document Modeling with External Attention for Sentence ExtractionCode0
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair GenerationCode0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
Building Large Machine Reading-Comprehension Datasets using Paragraph VectorsCode0
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