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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 426–450 of 555 papers

TitleStatusHype
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
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
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
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop—0
Composing Answer from Multi-spans for Reading Comprehension—0
Context Modeling with Evidence Filter for Multiple Choice Question Answering—0
Context-Paraphrase Enhanced Commonsense Question Answering—0
Continual Domain Adaptation for Machine Reading Comprehension—0
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation—0
Controlling Risk of Web Question Answering—0
Conversational Machine Comprehension: a Literature Review—0
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