[ACL2017]Enhanced LSTM for Natural Language Inference
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网络的大体框架:
图左侧是sequential model, 右侧是 Tree-LSTM model
自下而上由三个部分组成:
(1) input encoding
(2) local inference modeling
(3) inference composition
接下来针对每一部分给出详细解释:
第一部分:input encoding
A: sequential model 的做法
句子中的每个词都有了包含周围信息的 word representation
B: Tree-LSTM model的做法
树中的每个节点(短语或字句)有了向量表示 h
关于tree-LSTM 的介绍需要看文章:
[1] Improved semantic representations from tree-structured long short-term memory networks
[2] Natural Language inference by tree-based convolution and heuristic matching
[3] Long short-term memory over recursive structures
第二部分:Local Inference Modeling
A: sequential model
两句话相似或相反的对应
B: Tree-LSTM model
两棵树任意两个节点利用公式(11)计算
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