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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 201–225 of 555 papers

TitleStatusHype
On the Robustness of Reading Comprehension Models to Entity RenamingCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Multi-tasking Dialogue Comprehension with Discourse ParsingCode0
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
A Study on Contextualized Language Modeling for Machine Reading Comprehension—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension—0
Logic Pre-Training of Language Models—0
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple DocumentsCode1
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
Numerical reasoning in machine reading comprehension tasks: are we there yet?—0
Context-NER : Contextual Phrase Generation at ScaleCode1
An MRC Framework for Semantic Role LabelingCode1
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
RoR: Read-over-Read for Long Document Machine Reading ComprehensionCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading ComprehensionCode1
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Unsupervised Open-Domain Question Answering—0
Interactive Machine Comprehension with Dynamic Knowledge GraphsCode1
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension ModelsCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
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