Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories

Hikaru Asano¹ Ryo Yonetani² Taiki Sekii² Hiroki Ouchi^2,3

¹The University of Tokyo ²CyberAgent ³Nara Institute of Science and Technology

INLG 2024

📌 Overview

Text2Traj2Text is a learning-by-synthesis framework designed to generate natural language captions that describe the contextual backgrounds of shoppers' trajectory data in retail environments. The framework comprises two primary components:

Text2Traj: Generates customer behavior descriptions and corresponding trajectory data.
Traj2Text: Trains a model to convert trajectory data into natural language captions.

🛠 Prerequisites

We checked the reproducibility under the following environment.

Operating System: Ubuntu (≥22.04) or macOS
Docker: Version 24.0.7
Docker Compose: Version 2.23.1
CUDA 11.8.0: For GPU support
Python 3.9+

🔧 Setup

We recommend using Docker to manage dependencies. Follow the steps below to set up the environment.

1. Install Docker

If you haven't installed Docker yet, please follow the Docker Installation Guide.

2. Build and Run the Docker Container

Execute the following command to build and run the Docker container:

docker compose up -d

This command initializes a containerized environment with all necessary dependencies.

3. Preprocess Data

Before training, preprocess the data. Place the raw training data in the data directory and run:

bash scripts/preprocess.sh

🎓 Initial Experiment

To train and evaluate the Traj2Text model, execute the following command:

docker exec text2traj2text python3 scripts/train.py

By default, this command trains the model using the t5-small architecture with 8 paraphrased datasets.

🚀 Advanced: Full Reproduction of Our Experiment

To fully reproduce our experiments, you will need:

Access to the Azure OpenAI API (for Text2Traj dataset generation)
A GPU (for training models like t5-base and evaluating with LLaMA)

Customizing Training Parameters

You can customize the training process by specifying parameters. For example, to train the model using t5-base with 0 paraphrased data points:

docker exec text2traj2text python3 scripts/train.py train.model_name=t5-base dataset.num_paraphrase=0

⚙️ Generating Text2Traj Dataset

API Keys and Environment Variables

API keys are necessary for generating the Text2Traj dataset and evaluating models with LLaMA and OpenAI.

To generate the dataset or run evaluations, follow these steps:

Create a .env file in the root directory of the project.
Add the following content to the .env file:
```
AZURE_OPENAI_VERSION=
AZURE_OPENAI_ENDPOINT=
AZURE_OPENAI_API_KEY=
HUGGINGFACE_ACCESS_TOKEN=
```
- The Azure OpenAI API key is required for Text2Traj dataset generation and evaluation with ChatGPT.
- The Hugging Face access token is required for evaluation with LLaMA-2-7b.

To generate the Text2Traj dataset, follow these steps:

Generate User Captions:

docker exec text2traj2text python3 scripts/text2traj/generate_user_captions.py

Generate Purchase List:

docker exec text2traj2text python3 scripts/text2traj/generate_purchase_list.py

Generate Paraphrasing:

docker exec text2traj2text python3 scripts/text2traj/generate_paraphrasing.py

Generate Trajectory:

docker exec text2traj2text python3 scripts/text2traj/generate_trajectory.py

Execute All Steps at Once

To run all the above steps sequentially, use:

bash scripts/generate_user_activity.sh

Adjusting Generation Parameters

You can modify parameters such as num_generations and model_name directly in the script. For example, to generate 1000 data points using gpt-4o with a temperature of 0.7:

docker exec text2traj2text python3 scripts/text2traj/generate_user_captions.py num_generations=1000 model_name=gpt-4o temperature=0.7

Saving Generated Datasets

Generated datasets are stored in the data/raw_data/<project_name> directory. To specify a different project name:

docker exec text2traj2text python3 scripts/text2traj/generate_user_captions.py project_name=your_project_name

Training with the Generated Dataset

After dataset generation, preprocess it before training:

bash scripts/preprocess.sh your_project_name

📊 Evaluation with In-Context Learning

3.1 Evaluation with ChatGPT

To evaluate using GPT series models (e.g., GPT-4, GPT-3.5-turbo):

docker exec text2traj2text python3 scripts/eval_chatgpt.py

3.2 Evaluation with Open Source LLM

To evaluate using open-source language models (e.g., LLaMA-2-7b):

docker exec text2traj2text python3 scripts/eval_llm.py

📄 Citation

If you find our work useful in your research, please consider citing:

@inproceedings{asano2024text2traj2text,
    title={{Text2Traj2Text}: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories},
    author={Hikaru Asano and Ryo Yonetani and Taiki Sekii and Hiroki Ouchi},
    booktitle = {Proceedings of the 17th International Natural Language Generation Conference},
    year={2024},
    pages={289--302},
}

CyberAgentAILab/text2traj2text