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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 151–175 of 555 papers

TitleStatusHype
A Unified Abstractive Model for Generating Question-Answer Pairs—0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text—0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension—0
Effective Character-augmented Word Embedding for Machine Reading Comprehension—0
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes—0
Data Augmentation for Biomedical Factoid Question Answering—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
A Survey on Neural Machine Reading Comprehension—0
Attention-Guided Answer Distillation for Machine Reading Comprehension—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Deep Understanding based Multi-Document Machine Reading Comprehension—0
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval—0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension—0
Dialog State Tracking: A Neural Reading Comprehension Approach—0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
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