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Machine Reading Comprehension
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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.
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Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Towards Robust Text Retrieval with Progressive Learning
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension
Mirror: A Universal Framework for Various Information Extraction Tasks
Multi-grained Evidence Inference for Multi-choice Reading Comprehension
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers
Explaining Interactions Between Text Spans
Instructive Dialogue Summarization with Query Aggregations
Hierarchical Evaluation Framework: Best Practices for Human Evaluation
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change
Multi-turn Dialogue Comprehension from a Topic-aware Perspective
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model
Teach model to answer questions after comprehending the document
ZeQR: Zero-shot Query Reformulation for Conversational Search
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
SciMRC: Multi-perspective Scientific Machine Reading Comprehension
Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
A Causal View of Entity Bias in (Large) Language Models
Machine Reading Comprehension using Case-based Reasoning
mPMR: A Multilingual Pre-trained Machine Reader at Scale
EMBRACE: Evaluation and Modifications for Boosting RACE
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
Adaptive loose optimization for robust question answering
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