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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 1–10 of 555 papers

TitleStatusHype
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation—0
Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes FromCode0
Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension—0
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension BenchmarkCode0
Pay Attention to Real World Perturbations! Natural Robustness Evaluation in Machine Reading Comprehension—0
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-FollowingCode0
RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading ComprehensionCode0
Visualizing attention zones in machine reading comprehension models—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
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