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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 201–250 of 555 papers

TitleStatusHype
English Machine Reading Comprehension Datasets: A SurveyCode0
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
ARES: A Reading Comprehension Ensembling Service—0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—0
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model—0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
A Graph Fusion Approach to Cross-Lingual Machine Reading Comprehension—0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions—0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension—0
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension—0
A Constituent-Centric Neural Architecture for Reading Comprehension—0
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection—0
CFO: A Framework for Building Production NLP Systems—0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation—0
Feeding What You Need by Understanding What You Learned—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension—0
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks—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
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations—0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
Explicit Utilization of General Knowledge in Machine Reading Comprehension—0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension—0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
A Frame-based Sentence Representation for Machine Reading Comprehension—0
FQuAD: French Question Answering Dataset—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches—0
App-Aware Response Synthesis for User Reviews—0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension—0
Event Extraction as Machine Reading Comprehension—0
Event Detection via Derangement Reading Comprehension—0
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents—0
BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
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