# 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/](data/) folder. ## Environment Setup Python 3.6 is required for training the baseline model. Python 3.10 is required for training the prompt-based model. `conda` is used for creating the environments. Use `CONDA_ENVS_PATH` to set the custom path for storing the conda environments (if required) ```shell # optional export CONDA_ENVS_PATH=/path/to/custom/dir ``` ### Create conda environment (for baseline model) Create an environment for baseline training with a specific python version (Python 3.6 is **required**). ```shell conda create -n python=3.6 ``` ### Create conda environment (for prompt learning) Create an environment for prompt-based methods (Python 3.10 is **required**) ```shell conda create -n python=3.10 ``` #### Activate the conda environment To activate the conda environment, run: ```shell conda activate ``` #### Deactivating the conda environment 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 fine-tuning 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 source code ```shell git clone https://git.pavanmandava.com/pavan/master-thesis.git ``` Change directory ```shell cd master-thesis ``` Pull the changes from remote (if local is behind the remote) ```shell git pull ``` #### 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 (as required) for the following: `PRE_TRAINED_SOLOIST` - Path to the extracted pre-trained SOLOIST model `SAVED_MODELS_BASELINE` - Path for saving the trained baseline models (fine-tuning) at checkpoints `OUTPUTS_DIR_BASELINE` - Path for storing the baseline model outputs (belief state predictions) `SAVED_MODELS_PROMPT` - Path for saving the trained prompt-based models (after each epoch) `OUTPUTS_DIR_PROMPT` - Path for storing the prompt model outputs (generations) > :information_source: **Note**: Change the paths of each environment variable and make sure it matches your local system. Invalid/Wrong paths may lead to errors in the training/testing script. ```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 -r 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 ``` 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=// ``` 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 of the 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 experiments |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 | ## Prompt Learning Experiments ### Data The data for training the prompt learning model is available under [data/prompt-learning](data/prompt-learning) directory. `create_dataset.py` ([link](utils/create_dataset.py)) has the scripts for converting/creating the data for training the prompt-based model. > **Note:** > Running `create_dataset.py` can take some time as it needs to download, install and run Stanford CoreNLP `stanza` package. > All the data required for training the prompt-based model is available under [data](data) directory of this repo. ### Install the requirements After following the environment setup steps in the previous [section](#environment-setup), install the required python modules for prompt model training. Change directory to `prompt-learning` and install the requirements. Make sure the correct prompt-learning `conda` environment is activated before installing the requirements. ```shell cd prompt-learning pip install -r requirements.txt ``` ### Train the prompt model Train a separate model for each data split. Edit the [train_prompting.sh](prompt-learning/train_prompting.sh) file to modify the default hyperparameters for training (learning rate, epochs). ```shell sh train_prompting.sh -d ``` 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` **Some `train_prompting.sh` flags**: `--num_epochs 10` - Number of epochs `--learning_rate 5e-5` - Initial learning rate for Optimizer `--with_inverse_prompt` - Use Inverse Prompt while training `--inverse_prompt_weight 0.1` - Weight of the inverse prompt for loss function **Note:** The defaults in `train_prompting.sh` are the best performing values. ### Belief State Generations (Prompt Generation) Now, the belief states can be generated by prompting. Choose a prompt fine-tuned model from the saved epochs and run the below script to generate belief states. Generate belief states by running the below script: ```shell sh test_prompting.sh -m ``` The argument `-m` takes the relative path of saved model from `SAVED_MODELS_PROMPT` env variable. It takes the following structure `-m //` Example: `sh test_prompting.sh -m 50-dpd/experiment-20221003T172424/epoch-09` The generated belief states (outputs) are saved under `OUTPUTS_DIR_PROMPT` folder. Some of the best outputs are uploaded to this repository and can be found under [outputs](outputs) folder. ### Prompting Evaluation The standard Joint Goal Accuracy (JGA) is used to evaluate the belief predictions. Edit the [evaluate.py](prompt-learning/evaluate.py) to set the predictions output file before running the evaluation ```shell python evaluate.py ``` ### Results from prompt-based belief state generations |data-split| JGA* | |--|:--:| | 5-dpd | //TODO | | 10-dpd | //TODO | | 50-dpd | //TODO | | 100-dpd | //TODO | | 125-dpd | //TODO | | 250-dpd | //TODO | // TODO :: Add prompt-based outputs and results in the above table ## Multi-prompt Learning Experiments ### Prompt Ensemble **Training** Train a separate model for each data split. Edit the [train_prompting.sh](prompt-learning/train_prompting.sh) file and add `--with_prompt_ensemble` for training with multiple prompt functions. // TODO :: Add more README for training and generating. // WIP :: Prompt ensemble training ### Prompt Augmentation Prompt Augmentation, also called *demonstration learning*, provides a few additional *answered prompts* that can demonstrate to the PLM, how the actual prompt slot can be answered. Sample selection of answered prompts are manually hand-picked. Experiments are performed on different sets of *answered prompts*. Edit the [test_prompting.sh](prompt-learning/test_prompting.sh) file and add `--with_answered_prompts` flag for generating slots with answered prompts. Generate belief states by running the below script: ```shell sh test_prompting.sh -m ``` // TODO :: Add results