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OOD-robust Hybrid Code Networks

A suite of methods for improving robustness of task-oriented dialogue models.

Code for paper "Improving Robustness of Dialog Systems in a Data-Efficient Way with Turn Dropout" by Igor Shalyminov and Sungjin Lee. [Paper - presented at ConvAI Workshop@NeurIPS 2018] [Poster]

HCN implementation is based on https://github.com/johndpope/hcn

Repo with OOD-augmented data

Setup with Conda

  1. conda create -n ood_robust_hcn python=3.7 cython tensorflow-gpu==1.14.0

  2. conda activate ood_robust_hcn

  3. pip install -r requirements.txt

Dialog control with HCN

0.1 Download word2vec vectors:

cd hcn/data; sh get_word2vec.sh

0.2 Initialize the datasets

git submodule update --init

cd icassp-ood-dataset; unzip *.zip

  1. Training:

cd hcn; python train.py data ../icassp-ood-dataset/babi_task6 ../icassp-ood-dataset/babi_task6_ood_0.2_0.4 <model folder> configs/<config-json> [--custom_vocab <vocab file>]

  1. Evaluation:

cd hcn; python evaluate.py data ../icassp-ood-dataset/babi_task6 ../icassp-ood-dataset/babi_task6_ood_0.2_0.4 <model folder> [clean/noisy]

Standalone OOD detection

  1. Autoencoder-based

Making a dataset for AE:

python make_dataset_for_autoencoder.py hcn/data <result folder>

Training an AE:

cd ae_ood; python train_ae.py <AE dataset folder>/trainset <AE dataset folder>/devset <AE dataset folder>/testset <model folder>

Evaluating the AE:

cd ae_ood; python evaluate.py <model folder> <AE dataset folder>/devset <AE dataset folder>/evalset --decision_type [min/max/avg]

  1. VAE-based

Training a VAE:

cd vae; python train.py <AE dataset folder>/trainset <AE dataset folder>/devset <AE dataset folder>/testset <model folder>

Evaluating the VAE:

`cd vae; python evaluate_vae_ood.py /devset /evalset --decision_type [min/max/avg] --loss_components [kl_loss(,nll_loss)]``

Custom OOD data generation

bAbI Dialog Task 6 augmentation:

  1. Run the notebooks:

mining_ood_reddit.ipynb, mining_ood_twitter.ipynb, mining_foreign_domain_ood.ipynb, mining_ood_breakdown.ipynb

  1. cd babi_tools; sh make_ood_dataset.sh ../hcn/data ../data/babi_task6_ood_dataset_<parameters>

ood_augmentation.json config file will be used which sets the probabilities of OOD sequence start and continuation respectively.