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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 101–125 of 555 papers

TitleStatusHype
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—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
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