We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yxuansu/plangen | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| map_cuda | Ran | yxuansu/plangen/generator/finetune.py pointer only (licence: NONE) · get_code("4988e6e0ed6c0697") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2108.13740")
get_code_for_paper("2108.13740")
have("2108.13740")
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