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• W2951719623 abstract "Automatic translation from natural language descriptions into programs is a longstanding challenging problem. In this work, we consider a simple yet important sub-problem: translation from textual descriptions to If-Then programs. We devise a novel neural network architecture for this task which we train end-to-end. Specifically, we introduce Latent Attention, which computes multiplicative weights for the words in the description in a two-stage process with the goal of better leveraging the natural language structures that indicate the relevant parts for predicting program elements. Our architecture reduces the error rate by 28.57% compared to prior art. We also propose a one-shot learning scenario of If-Then program synthesis and simulate it with our existing dataset. We demonstrate a variation on the training procedure for this scenario that outperforms the original procedure, significantly closing the gap to the model trained with all data." @default.
• W2951719623 created "2019-06-27" @default.
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• W2951719623 date "2016-11-07" @default.
• W2951719623 modified "2023-09-28" @default.
• W2951719623 title "Latent Attention For If-Then Program Synthesis" @default.
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• W2951719623 hasPublicationYear "2016" @default.
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