Tree-to-tree Neural Networks for Program Translation
Authors: Xinyun Chen, Chang Liu, Dawn Song
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. Compared against other neural translation models, we observe that our approach is consistently better than the baselines with a margin of up to 15 points. Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects. |
| Researcher Affiliation | Academia | Xinyun Chen UC Berkeley xinyun.chen@berkeley.edu Chang Liu UC Berkeley liuchang2005acm@gmail.com Dawn Song UC Berkeley dawnsong@cs.berkeley.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | No | The paper describes the generation and characteristics of its datasets, and refers to using existing open-source projects for one dataset, but it does not provide concrete access information (link, DOI, formal citation of a public dataset) for the specific datasets used in their experiments. The grammar for generating data is mentioned as being in supplementary material, but not the generated dataset itself. |
| Dataset Splits | Yes | To build the dataset, we randomly generate 100,000 pairs of source and target programs for training, 10,000 pairs as the development set, and 10,000 pairs for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software components but does not provide specific version numbers for reproducibility. |
| Experiment Setup | No | The hyper-parameters used in different models can be found in the supplementary material. This means that the specific experimental setup details are not provided in the main text of the paper. |