Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding

This repository contains code and instructions for reproducing the experiments in the paper The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding (Findings of ACL 2023). [paper] [video]

Introduction (WIP)

There are two key components in our framework: dataflow transduction and constrained decoding, implemented by the dataflow2text package and the clamp package, respectively.

• These two packages currently use two different Python versions. The dataflow2text package relies on the structural pattern matching feature introduced in Python 3.10, whereas the clamp package heavily relies on PyTorch and 🤗 Transformers.
• The clamp package is a simplified version of the code for Semantic Parsing with Constrained LM.

To reproduce the SMCalFlow2Text results reported in the paper, please refer to the worksheets folder. You will need to create two python virtual environments.

conda env create --file=dataflow2text/environment.yml --name=dataflow2text_py310

conda env create --file=clamp/environment.yml --name=clamp_py37

More details coming soon!

Citation

If you use any source code or data included in this repo, please cite our paper.

@article{SMCalflow2Text2023,
    title={The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding}, 
    author={Hao Fang and 
      Anusha Balakrishnan and 
      Harsh Jhamtani and 
      John Bufe and 
      Jean Crawford and 
      Jayant Krishnamurthy and 
      Adam Pauls and 
      Jason Eisner and 
      Jacob Andreas and 
      Dan Klein},
    booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
    year={2023},
}