/Reader_Model

This code accompanies the paper: Guiding Neural Story Generation with Reader Models

Primary LanguagePython

Reader Model

This code accompanies the paper Guiding Neural Story Generation with Reader Models

💥 Installation

  1. Virtual env: conda env create -f environment.yml python=3.6

  2. Install the COMeT

    • Follow the instruction in the COMET original repo.
    • There are some extra packages you need to install here.
    conda install -c pytorch pytorch
pip install ftfy

    • First, download the pretrained models from here.

    • Then untar the file:

    tar -xvzf pretrained_models.tar.gz
    • Then run the following script to interactively generate arbitrary ATOMIC event effects:
      python scripts/interactive/atomic_single_example.py --model_file pretrained_models/atomic_pretrained_model.pickle

    • Some absolute paths have to be changed after you clone the repo ./comet/comet-commonsense/src/interactive/functions.py.

    • How to use COMeT to convert sentence as knowledge graph?

    python KG_gen/KG_gen.py

  3. Install verbatlas SRL

    • The file is located at ./verbatlas/src
    • Installation follows here

  4. No need to prepare for verbnet parser. Location: ./semparse-core/

  5. Install allennlp and allennlp-model.

  6. Install new comet-atomic 2020 from AI2.

Possible Bugs!!!

  1. Pattern cannot used in python 3.7 Solution

  2. No module named '_ctypes'

sudo apt-get install libffi-dev
  1. When run SRL, get __init__() got an unexpected keyword argument 'max_n'
pip uninstall py-rouge
pip install py-rouge

Pipeline for RM in KG-gen

cd verbatlas/src&&gunicorn --workers 4 --bind 0.0.0.0:5000 wsgi:app
cd verbnet-approach/src&&python main.py --trainer_type KG_diff_file --topk 2 --story_length 3 --KG_use --remain_div
cd verbnet-approach/src&&python main.py --trainer_type KG_diff_step_file --topk 5 --story_length 10 --KG_use --remain_div --look_ahead 1 --seed 3

❣️ KG / State Transition

Given a state, RM will output action space and then human choose an action, and RM will generate a new state. This is the code you need to run in the command line. However, there might need some modification for the absolute path. I have tried my best to convert all the absolute path to relative ones. Pls LMK if there still exist any absolute path. Thanks.

💦 Functions

For generating action space and new state, the main file is here.

  • define_roles(): Find role/ character names in the sentence.
  • prepare_next_step(): Use the parser, construct or add nodes in KG
  • generate_actions(): Given the KG and then generate all the possible action space.
  • forward(): Different round will trigger different functions. It will be called in main.py and move one sentence forward.

🤩 Features

  • trainer_type: QA or generation or KG or KG_diff or KG_diff_file, We are using KG_diff_file as default.
  • model_name_or_path: GPT-2's pretrained model path
  • mapping_file and verbnet_json: path of verbnet files.
  • target_path: path to save kg visualization
  • model_file: COMeT path
  • BART is used to fill in the verbs. The model is facebook/bart-large
  • GPT-2 finetuned on ROC is used to generate the sentence after we have verb and noun.
  • --KG_use: whether to use KG to update the game state
  • --topk: int, # of sentence for each round we remain.
  • --action_standard: Use verb or sentence as actions. Now we are using sentence as default.
  • --remain_div: half of the candidates have different prompts
  • --story_length: length of the generated story
  • --look_ahead: only apply to trainer_type=KG_diff_step_file or trainer_type=KG_diff_step, m_1 in the paper.