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relational_semantic_network [2018/08/31 15:22]
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relational_semantic_network [2018/10/08 12:37]
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 We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture combines standard neural entailment models with a knowledge lookup module. To facilitate this lookup, we propose a fact-level decomposition of the hypothesis, and verifying the resulting sub-facts against both the textual premise and the structured KB. Our model, NSnet, learns to aggregate predictions from these heterogeneous data formats. ​ We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture combines standard neural entailment models with a knowledge lookup module. To facilitate this lookup, we propose a fact-level decomposition of the hypothesis, and verifying the resulting sub-facts against both the textual premise and the structured KB. Our model, NSnet, learns to aggregate predictions from these heterogeneous data formats. ​
  
 +https://​arxiv.org/​pdf/​1806.01445v2.pdf Embedding Logical Queries on Knowledge Graphs
  
 +https://​github.com/​williamleif/​graphqembed
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 +https://​arxiv.org/​abs/​1806.01822v2 Relational recurrent neural networks
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 +We then improve upon these deficits by using a new memory module -- a {Relational Memory Core} (RMC) -- which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information,​ and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103,​ Project Gutenberg, and GigaWord datasets. https://​github.com/​deepmind/​sonnet/​blob/​master/​sonnet/​python/​modules/​relational_memory.py
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 +https://​slideslive.com/​38909774/​embedding-symbolic-computation-within-neural-computation-for-ai-and-nlp
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 +https://​arxiv.org/​abs/​1809.11044 Relational Forward Models for Multi-Agent Learning