Memory Networks
Memory Networks 是一种框架，在这个框架内部的每个module都可以根据特定任务的需要用不同的方式来实现（task-specific）。
本篇主要以 《End-to-End Memory Networks》 (2015, MemN2N) 对 Memory Network 的框架进行介绍
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ICLR2019 | Coarse-Grain Fine-Grain Coattention Network for Multi-Evidence Question Answering
Coarse-Grain Fine-Grain Coattention Network for Multi-Evidence Question AnsweringICLR 2019Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, Richard
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Analysis of Multi-Passage RC Task
Works of multi-passage MRC taskTarget Datasets: MS MARCO, Dureader
Reference papers on this track:
Multi-Passage Machine Reading Comprehension with
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Analysis of Multi-Choice RC Task
focus on the strategy of matching processing between (P, Q, Ans)
target datasets: RACE, MCScripts
Reference papers on multi-choice MRC task, especia
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Practicable Course List (continually updated)
The main areas of concern are:
Natural Language ProcessingMachine Learning and Deep LearningReinforcement LearningFoundation of Mathematics
NLP
Stan
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Knowledge-based MRC Papers
A list of recent papers with respect to Knowledge-based Machine Reading Comprehension.
2018 | Dynamic Integration of Background Knowledge in Neural NLU Systems
Dynamic Integration of Background Knowledge in Neural NLU SystemsICLR,2018. RejectDirk Weissenborn, et.al.Datasets: SQuAD, TriviaQA, SNLI, MNLI
Moti
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ACL2018 | Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question AnsweringACL 2018Wei Wang, Ming Yan, Chen Wu.Alibaba
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EMNLP2018 | Commonsense for Generative Multi-Hop Question Answering Tasks
Commonsense for Generative Multi-Hop Question Answering TasksEMNLP 2018Lisa Bauer et al. UNC.Multi-Hop reasoning; generative-mrc; commmonsense knowle
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Commonsense to Controllable TG
本文将介绍 两篇 关于利用常识知识进行可控地文本生成的论文，都是清华大学黄民烈老师组的研究工作：
Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI,2018.Story Ending Ge
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