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arXiv · 1611.01867

Latent Attention For If-Then Program Synthesis

Xinyun Chen·Chang Liu·Richard Shin·Dawn Song·Mingcheng Chen
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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.

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Xinyun Chen, Chang Liu, Richard Shin, Dawn Song, Mingcheng Chen. 2016-11-07. Latent Attention For If-Then Program Synthesis. https://arxiv.org/abs/1611.01867

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