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Latent Predictor Networks for Code Generation
Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
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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Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | Django | lpn (Ling et al., 2016) | Accuracy | 62.3 | #10 of 11 | Archive leaderboard | report |
| Code Generation | Django | lpn (Ling et al., 2016) | BLEU Score | 77.6 | #10 of 11 | Archive leaderboard | report |
| Code Generation | Django | Phrasal Statistical MT (Ling et al., 2016) | Accuracy | 31.5 | #11 of 11 | Archive leaderboard | report |
| Code Generation | Django | Phrasal Statistical MT (Ling et al., 2016) | BLEU Score | 47.6 | #11 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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