# Prompt-based methods for Dialog State Tracking  
  
Repository for my master thesis at the University of Stuttgart (IMS).  
  
Refer to this thesis [proposal](proposal/proposal_submission_1st.pdf) document for detailed explanation about thesis experiments.  
    
## Dataset
MultiWOZ 2.1 [dataset](https://github.com/budzianowski/multiwoz/blob/master/data/MultiWOZ_2.1.zip) is used for training and evaluation of the baseline/prompt-based methods. MultiWOZ is a fully-labeled dataset with a collection of human-human written conversations spanning over multiple domains and topics. Only single-domain dialogues are used in this setup for training and testing. Each dialogue contains multiple turns and may also contain a subdomain *booking*. Five domains - *Hotel, Train, Restaurant, Attraction, Taxi* are used in the experiments and excluded the other two domains as they only appear in the training set. Under few-shot settings, only a portion of the training data is utilized to measure the performance of the DST task in a low-resource scenario. Dialogues are randomly picked for each domain. The below table contains some statistics of the dataset and data splits for the few-shot experiments.

| Data Split | # Dialogues | # Total Turns |
|--|:--:|:--:|
| 5-dpd | 25 | 100 |
| 10-dpd | 50 | 234 |
| 50-dpd | 250 | 1114 |
| 100-dpd | 500 | 2292 |
| 125-dpd | 625 | 2831 |
| 250-dpd | 1125 | 5187 |
| valid | 190 | 900 |
| test | 193 | 894 |

In the above table, term "*dpd*" refers to "*dialogues per domain*". For example, *50-dpd* means *50 dialogues per each domain*. 

All the training and testing data can be found under [/data/baseline/](data/baseline) folder.

## Environment Setup
Python 3.6 is required for training the baseline model. `conda` is used for creating environments.

### Create conda environment (for baseline model)
Create an environment for baseline training with a specific python version (Python 3.6).
```shell
conda create -n <baseline-env-name> python=3.6
```
### Create conda environment (for prompt learning)
Create an environment for prompt-based methods
```shell
# TODO
```

#### Activate the conda environment
To activate the conda environment, run:
```shell
conda activate <env-name>
```

#### Deactivating the conda evironment
To deactivate the conda environment, run: (Only after running all the experiments)
```shell
conda deactivate
```
#### Download and extract SOLOIST pre-trained model
Download and unzip the pretrained model, this is used for finetuning the baseline and prompt-based methods. For more details about the pre-trained SOLOIST model, refer to the GitHub [repo](https://github.com/pengbaolin/soloist).

Download the zip file, replace the `/path/to/folder` from the below command to a folder of your choice.
```shell
wget https://bapengstorage.blob.core.windows.net/soloist/gtg_pretrained.tar.gz \
	-P /path/to/folder/
```

Extract the downloaded pretrained model zip file.
```shell
tar -xvf /path/to/folder/gtg_pretrained.tar.gz
```

#### Clone the repository
Clone the repository for source code
```shell
git clone https://git.pavanmandava.com/pavan/master-thesis.git
```
Pull the changes from remote (if local is behind the remote)
```shell
git pull
```
Change directory
```shell
cd master-thesis
```

#### Set Environment variables
Next step is to set environment variables that contains path to pre-trained model, saved models and output dirs.

Edit the [set_env.sh](set_env.sh) file and set the paths for: (`nano` or `vim` can be used)

`PRE_TRAINED_SOLOIST` - Path to the extracted pre-trained SOLOIST model

`SAVED_MODELS_BASELINE` - Path for saving the trained models at checkpoints

`OUTPUTS_DIR_BASELINE` - Path for storing the outputs of belief state predictions.

```shell
nano set_env.sh
```
Save the edited file and `source` it
```shell
source set_env.sh
```
Run the below line to unset the environment variables
```shell
sh unset_env.sh
```

## Baseline Experiments
SOLOIST ([Peng et al., 2021](https://arxiv.org/abs/2005.05298)), the baseline model for this thesis, is a task-oriented dialog system that uses transfer learning and machine teaching to build task bots at scale. SOLOIST uses the pre-train, fine-tune paradigm for building end-to-end dialog systems using a transformer-based auto-regressive language model GPT-2. In the pre-training stage, SOLOIST is initialized with 12-layer GPT-2 (117M parameters) and further trained on two task-oriented dialog corpora for solving *belief state prediction* task. In the fine-tuning stage, the pre-trained SOLOIST is fine-tuned on MultiWOZ 2.1 dataset to perform belief prediction task.

### Install the requirements
After following the environment setup steps in the previous [section](#environment-setup), install the required python modules for baseline model training.

Change directory to `baseline` and install the requirements. Make sure the correct baseline conda environment is activated before installing the requirements.
```shell
cd baseline
pip install requirements.txt
```

### Train the baseline model
Train a separate model for each data split. Edit the [train_baseline.sh](baseline/train_baseline.sh) file to modify the hyperparameters while training (learning rate, epochs). Use `CUDA_VISIBLE_DEVICES` to specify a CUDA device (GPU) for training the model. 
```shell
sh train_baseline.sh -d <data-split-name>
```
Pass the data split name to `-d` flag. Possible values are: `5-dpd`, `10-dpd`, `50-dpd`, `100-dpd`, `125-dpd`, `250-dpd`

Example training command: `sh train_baseline.sh -d 50-dpd`

### Belief State Prediction
Choose a checkpoint of the saved baseline model to generate belief state predictions.

Set the `MODEL_CHECKPOINT` environment variable with the path to the chosen model checkpoint. It should only contain the path from the "experiment-{datetime}" folder.
```shell
export MODEL_CHECKPOINT=<experiment-folder>/<data-split-name>/<checkpoint-folder>
```
Example: `export MODEL_CHECKPOINT=experiment-20220831/100-dpd/checkpoint-90000`

Generate belief states by running decode script
```shell
sh decode_baseline.sh
```
The generated predictions are saved under `OUTPUTS_DIR_BASELINE` folder. Some generated belief state predictions are uploaded to this repository and can be found under [outputs](outputs) folder.

### Baseline Evaluation

The standard Joint Goal Accuracy (JGA) is used to evaluate the belief predictions. This metric compares all the predicted belief states to the ground-truth states for each turn. The prediction is considered correct only if all the predicted belief states match with the ground-truth states. Both slots and values must match for the prediction to be correct.

Edit the [evaluate.py](baseline/evaluate.py) to set the predictions output file before running the evaluation
```shell
python evaluate.py
```
#### Results from baseline evaluation
|data-split| JGA |
|--|:--:|
| 5-dpd | 9.06 |
| 10-dpd | 14.20 |
| 50-dpd | 28.64 |
| 100-dpd | 33.11 |
| 125-dpd | 35.79 |
| 250-dpd | 40.38 |
 
