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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 541–550 of 555 papers

TitleStatusHype
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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