Welcome to an exciting journey into the world of Natural Question Generation (NQG) using a cutting-edge reinforcement learning approach! In this article, we will walk you through the process required to set up and run the Graph-to-Sequence model for NQG based on the ICLR 2020 paper.

Prerequisites

Before diving into the setup, ensure you have Python 3 installed on your system. To ensure smooth operations, we recommend managing your packages and environments using virtualenv. Here’s how you can get started:

• Install virtualenv:

  pip install virtualenv

• Create a virtual environment:

  virtualenv venv

• Activate the virtual environment:

  source venv/bin/activate

• Install the required packages:

  pip install -r requirements.txt

Running the Model

Now that your environment is ready, it’s time to run the model! Follow these steps:

• Download the preprocessed data from
  squad-split1 and
  squad-split2.
  Place the downloaded data in the root directory, organizing it like so:
  • data/squad-split1
  • data/squad-split2
• Run the model using:

  python main.py -config config/squad_split1/graph2seq_static_bert_finetune_word_70k_0.4_bs_60.yml

  Note: You can specify the output path by changing the out_dir parameter in the config file.

• To finetune a pretrained model, modify the pretrained field and set out_dir to null.
• If you just want to load a pretrained model to evaluate on a test set, ensure both trainset and devset are set to null.
• For finetuning the model using reinforcement learning, execute:

  python main.py -config config/squad_split1/rl_graph2seq_static_bert_finetune_word_70k_0.4_bs_60.yml

Understanding the Code: An Analogy

Imagine you’re building a LEGO castle. Each step of the process requires specific pieces and careful assembly. The initial setup in the README is like gathering and sorting your LEGO pieces. Creating the virtual environment is akin to your dedicated workspace, ensuring every piece is in order and ready for assembly.

Downloading the datasets is similar to collecting the base LEGO blocks for your castle foundation. Running the model for various configurations (finetuning with RL, etc.) is equivalent to following specific construction guides to create unique features in your castle, such as towers or walls, ensuring that your castle (the model) is built to specification.

Troubleshooting

If you encounter issues while setting up or running the model, consider the following troubleshooting steps:

• Ensure all packages in requirements.txt are correctly installed. You might need to upgrade certain packages or Python itself.
• If you face file not found errors, double-check the path locations of the downloaded datasets to ensure they are correctly placed.
• Check your configuration file for any typos in the paths or settings.
• If errors occur during model execution, refer to the specific line indicated by the error message for targeted troubleshooting.

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Conclusion

With this guide, you should be well-equipped to set up and run the Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. Happy coding!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.