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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 541–550 of 555 papers

TitleStatusHype
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)—0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation—0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information—0
Knowledge Based Machine Reading Comprehension—0
Know your tools well: Better and faster QA with synthetic examples—0
KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension—0
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