Latent Predictor Networks for Code Generation

Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Code Generation Django lpn (Ling et al., 2016) Accuracy 62.3 # 6
BLEU Score 77.6 # 3
Code Generation Django Phrasal Statistical MT (Ling et al., 2016) Accuracy 31.5 # 7
BLEU Score 47.6 # 4


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