Continual Learning for Natural Language Generation

Getting started

The incremental folder contains the dataset, source code and configurations to reproduce our results.

Prerequisites

The following modules are necessary to run the experiments

• nltk 3.4.5 conda install -c anaconda nltk
• PyTorch 1.3.1 conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
• pandas 0.20.3 conda install -c anaconda pandas
• numpy 1.11.3 conda instal -c anaconca numpy

Code structure

.
├── model/                      # The model implementation containing the sclstm layers  
│  ├── lm_deep.py               # SCLSTM basedimplementation of utterance generation
│  ├── cvae.py                  # SCVAE based implementation of utterance generation
│  ├── masked_cross_entropy.py  # Masked cross entropy loss
│  ├── masked_kl_divergence.py  # Masked KL-divergence loss
│  ├── model_util.py            # Some helper functions of model
│  ├── layers/                  # The unique domain data split
│  │   ├── sclstm.py            # SCLSTM network
│  │   ├── encoder.py           # Encoder for SCVAE
│  │   └── decoder_deep.py      # SCLSTM based decoder
├── loader                      # Dataset loader
│  ├── dataset_woz3.py          # Loader of original dataset
│  └── task.py                  # Loader of task and implementation of examplars
├── resource/                   # Data source
│  ├── data_split/*.json        # Data split file
│  ├── feat_*.json              # Feature file containing dialogue acts and slot-values
│  ├── text_*.txt               # Text file with original and delexicalized utterances
│  ├── vacab.txt                # Vocabulary
│  └── template.txt             # The template containing possible domain-action-slot-value
├── ewc.py                      # Compute ewc penalty
├── bleu.py                     # Compute bleu score
├── run_woz3.py                 # Code for starting training, evaluating and testing
├── run.sh                      # Script to run experiment
├── config/*.cfg                # Experiment configuration 
├── experiments/experiment_name # Model and results of experiments
│  ├── model/                   # Models throughout the training of task sequence, notice that the intermediate model.pt contains the test results on all tasks till current task
│  ├── examplars/               # Examplars of each task
│  ├── experiment_name.log      # Training log
│  └── experiment_name.res      # Testing result
├── view_results.ipynb          # Summarize statistics of experiments
└── 
...

Experiment helper functions

.
├── split_multi_task.py                 # Script for generating utterances with unique task (domain/dialogue act), run this only if you need to re-generate feat.json, text.json and data split
├── construct_examplar.py               # Construct exemplars with `herding`, `random` or `prioritized (loss)` based method
└── 
...

Run the code

1. The default datasets have already been available in resource. However, to generate dataset from scratch, please run bash preprocess.sh ${split_type} to generate single domain text, feature and datasplit json file. The split_type can be either do for unique-domain utterances or da for unique dialogue-act utterances.

2. Create your configuration file, the default is config/config.cfg. Please go to config/ for detailed explanations.

3. Run bash run.sh ${train/test/recover} ${config_file} to reproduce our results or conduct extensive experiments.

p.s. Default settings are provided, but hyper-parameters of different methods can be set as comman line arguments to run_woz3.py

Check the result

• Continual learning results in each task are stored in experiments/${experiment_name}/*.log.

• To check aggregated results in our paper, run the notebook view_results.ipynb

• The generated utterances after running test mode are stored in experiments/${experiment_name}/*.res.