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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 251–275 of 555 papers

TitleStatusHype
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
Know your tools well: Better and faster QA with synthetic examples—0
A Unified Abstractive Model for Generating Question-Answer Pairs—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Multi-tasking Dialogue Comprehension with Discourse ParsingCode0
A Study on Contextualized Language Modeling for Machine Reading Comprehension—0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension—0
Logic Pre-Training of Language Models—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
Numerical reasoning in machine reading comprehension tasks: are we there yet?—0
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
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
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension ModelsCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
A New Entity Extraction Method Based on Machine Reading Comprehension—0
An Intelligent Recommendation-cum-Reminder System—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification—0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension—0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)—0
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)—0
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