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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 101–125 of 555 papers

TitleStatusHype
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension—0
Context-Paraphrase Enhanced Commonsense Question Answering—0
Composing Answer from Multi-spans for Reading Comprehension—0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension—0
Answer Generation through Unified Memories over Multiple Passages—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension—0
BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity—0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling—0
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations—0
CALOR-QUEST : un corpus d'entra\^ et d'\'evaluation pour la compr\'ehension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
CFO: A Framework for Building Production NLP Systems—0
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
An MRC Framework for Semantic Role Labeling—0
Biomedical Question Answering: A Survey of Approaches and Challenges—0
Adversarial reading networks for machine comprehension—0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph—0
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