The basic idea is that two RNN captures different aspects of input sequences from both identifiers and code context.
Next, the output of two RNNs is concatenated into a single vector, which is passed through a fully-connected linear layer.
The final linear layer maps the learned type annotation into a high-dimensional feature space, called Type Clusters. In order to create Type Clusters, we need to formulate the type prediction task as a similarity learning problem, rather than a classification problem.
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