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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 376–400 of 555 papers

TitleStatusHype
Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondCode1
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
To Test Machine Comprehension, Start by Defining Comprehension—0
Teaching Machine Comprehension with Compositional ExplanationsCode1
NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension—0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension—0
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora—0
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts—0
Clinical Reading Comprehension: A Thorough Analysis of the emrQA DatasetCode1
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions—0
Knowledgeable Dialogue Reading Comprehension on Key Turns—0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
Semantics-Aware Inferential Network for Natural Language Understanding—0
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World ApplicationsCode0
Answer Generation through Unified Memories over Multiple Passages—0
Logic-Guided Data Augmentation and Regularization for Consistent Question AnsweringCode1
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response DetectionCode0
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse StructureCode1
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