GXimingLu / neurologic_decoding

Neurologic Decoding

This is the official repo for the paper "Neurologic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints" (NAACL 2021)

Requirement

We suggest using conda to setup environment. With conda installed, create three new environments:

• Huggingface

  conda create -n hug python=3.7
  conda activate hug
  pip install -r huggingface.txt
  

• Fairseq

  You need to first replace ${HOME_PATH} in fairseq.yml with your home path.

  conda env create -f fairseq.yml

  You could also refer to installation instructions in fairseq repo.
  
  

• Unilm

  You need to first replace ${HOME_PATH} in unilm.yml with your home path.

  conda env create -f unilm.yml

  Then, you need to install a specific version of huggingface package which incorporates unilm following instructions here.

Our code is tested on Quadro RTX 8000 with CUDA version 11.2 and driver version 460.27.04

Model

We release our models trained on CommonGen data for reproducibility.

• GPT2: https://drive.google.com/drive/folders/1Jqav26p_g6BmpNg-6mx0AMiPYHH07Vju?usp=sharing
• BART: https://drive.google.com/drive/folders/19UUug_dkZbSltw1P-CrH_G9gKQwBuAA7?usp=sharing
• T5: https://drive.google.com/drive/folders/1sMQYbhHjp5p0MuzerQT8zS6yAXyIej5g?usp=sharing
• Unilm: https://drive.google.com/drive/folders/1V6ynPZE5XgAOIkPtb7_Npskz7S0OLp1N?usp=sharing
• Unilm_v2: https://drive.google.com/drive/folders/1ukVAg2RaCQqZXh1RtkPGBCleW8Xah7u8?usp=sharing

Please follow instructions in each sub-directory to run neurologic decoding on top of each model.

Evaluation

We adapt evaluation script from CommonGen for automatic metrics. If you couldn't install ROUGE successfully, please refer here for troubleshooting.

Citation

If you use this codebase in your work, please consider citing our paper:

@inproceedings{lu-etal-2021-neurologic,
    title = "{N}euro{L}ogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints",
    author = "Lu, Ximing  and  West, Peter  and  Zellers, Rowan  and  Le Bras, Ronan  and  Bhagavatula, Chandra  and  Choi, Yejin",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.339",
    doi = "10.18653/v1/2021.naacl-main.339",
    pages = "4288--4299",
    abstract = "Conditional text generation often requires lexical constraints, i.e., which words should or shouldn{'}t be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models {--} supervised or not {--} to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search. Empirical results on four benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised models with NeuroLogic Decoding often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks. Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.",
}