In an era where information overload is commonplace, generating concise and relevant keyphrases from large texts has become essential. This blog guides you on how to utilize the code from our ACL 19 paper Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards. Let’s dive into creating your keyphrase generation model!

Understanding the Architecture

Before we jump into coding, think of our keyphrase generation model as a chef trying to create a delightful dish from a list of ingredients (words). Just as a skilled chef refines recipes by experimenting and adjusting taste profiles, our model employs reinforcement learning to refine keyphrase generation. The ingredients include datasets, dependencies, and preprocessing scripts to get the essential flavors just right.

Prerequisites

• Python version 3.5+
• Pytorch version 0.4

Getting Started

Follow these steps to prepare your environment and dataset:

1. Download the Dataset

The necessary datasets can be downloaded from here. Unzip the files into the .data directory.

2. Data Preparation

Utilize the preprocess.py script to numericalize your source-target pairs. This means arranging all items correctly, just like a chef organizing ingredients. Run:

python3 preprocess.py -data_dir .data/kp20k_sorted -remove_eos -include_peos

3. Training Your Model

It’s time to train your baseline model using maximum-likelihood loss. The commands may look a bit daunting at first, but let’s simplify it:

• For a regular model, use:

  python3 train.py -data .data/kp20k_sorted -vocab .data/kp20k_sorted -exp_path exp%s.%s -exp kp20k -epochs 20 -copy_attention -train_ml -one2many -one2many_mode 1 -batch_size 12 -seed 9527

• Customize further using other flags as per your requirement, such as the -orthogonal_loss flag for orthogonal loss adjustments.

4. Training with Reinforcement Learning

To create a more nuanced model, implement reinforcement learning with the following:

python3 train.py -data .data/kp20k_separated -vocab .data/kp20k_separated -exp_path exp%s.%s -exp kp20k -epochs 20 -copy_attention -train_rl -one2many -one2many_mode 1 -batch_size 32 -separate_present_absent -pretrained_model [path_to_ml_pretrained_model] -max_length 60 -seed 9527

Troubleshooting

If you encounter issues during any of the steps, here are some troubleshooting tips:

• Ensure the correct versions of Python and PyTorch are installed.
• Double-check that your paths to data files and models are correct.
• Use the command python3 evaluate_prediction.py to compute evaluation scores and check for discrepancies.
• Adjust batch sizes as necessary based on your system’s capabilities.

If the model seems stuck or unresponsive, consider restarting the training process or checking the logs for any errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Wrapping Up

Embarking on the journey of keyphrase generation through our reinforcement learning approach not only enhances your coding skills but also contributes valuable insights into AI. 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.

References

• Abigail See, Peter J. Liu, Christopher D. Manning: Get To The Point: Summarization with Pointer-Generator Networks. ACL 2017.
• Rui Meng et al.: Deep Keyphrase Generation. ACL 2017.
• Hai Ye, Lu Wang: Semi-Supervised Learning for Neural Keyphrase Generation. EMNLP 2018.