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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 101–150 of 555 papers

TitleStatusHype
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—0
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
From Multiple-Choice to Extractive QA: A Case Study for English and ArabicCode0
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering—0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information—0
Interpreting Themes from Educational StoriesCode0
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0—0
WangchanLion and WangchanX MRC EvalCode0
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training—0
QASE Enhanced PLMs: Improved Control in Text Generation for MRC—0
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading ComprehensionCode0
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition—0
Towards Efficient Methods in Medical Question Answering using Knowledge Graph EmbeddingsCode0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
Towards Robust Text Retrieval with Progressive LearningCode0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
Multi-grained Evidence Inference for Multi-choice Reading Comprehension—0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
Explaining Interactions Between Text SpansCode0
Instructive Dialogue Summarization with Query AggregationsCode0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation—0
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning ApproachCode0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change—0
Multi-turn Dialogue Comprehension from a Topic-aware Perspective—0
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
Single-Sentence Reader: A Novel Approach for Addressing Answer Position BiasCode0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model—0
ZeQR: Zero-shot Query Reformulation for Conversational SearchCode0
Teach model to answer questions after comprehending the document—0
SciMRC: Multi-perspective Scientific Machine Reading Comprehension—0
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity LinkingCode0
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite—0
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User ManualsCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
A Causal View of Entity Bias in (Large) Language ModelsCode0
Machine Reading Comprehension using Case-based Reasoning—0
mPMR: A Multilingual Pre-trained Machine Reader at ScaleCode0
EMBRACE: Evaluation and Modifications for Boosting RACECode0
SkillQG: Learning to Generate Question for Reading Comprehension Assessment—0
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension—0
Adaptive loose optimization for robust question answeringCode0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets—0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension—0
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