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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 226–250 of 555 papers

TitleStatusHype
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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