**EmoUS** is a data-driven user simulator with transformers, generating user emotions, semantic actions, and natural language responses based on the user goal, the dialogue history, and the user persona.
We propose a generative transform-based user simulator (EmoUS) in this work. EmoUS consists of an encoder-decoder structure, which can generate user emotions, semantic actions, and natural language responses based on the user goal, the dialogue history, and the user persona. By analysing what kind of system behaviour elicits what kind of user emotions, we show that EmoUS can be used as a probe to evaluate a variety of dialogue systems and in particular their effect on the user's emotional state. Developing such methods is important in the age of large language model chat-bots and rising ethical concerns.
**ConvLab-3** is a flexible dialog system platform based on a **unified data format** for task-oriented dialog (TOD) datasets. The unified format serves as the adapter between TOD datasets and models: datasets are first transformed to the unified format and then loaded by models. In this way, the cost of adapting $M$ models to $N$ datasets is reduced from $M\times N$ to $M+N$. While retaining all features of [ConvLab-2](https://github.com/thu-coai/ConvLab-2), ConvLab-3 greatly enlarges supported datasets and models thanks to the unified format, and enhances the utility of reinforcement learning (RL) toolkit for dialog policy module. For typical usage, see our [paper](http://arxiv.org/abs/2211.17148). Datasets and Trained models are also available on [Hugging Face Hub](https://huggingface.co/ConvLab).
This repo is a snapshot of current convlab-3 and you can find the code of EmoUS is in `convlab/policy/emoUS`.
-[Installation](#installation)
-[Tutorials](#tutorials)
-[Unified Datasets](#unified-datasets)
-[Models](#models)
-[Contributing](#contributing)
-[Code Structure](#code-structure)
-[Team](#team)
-[Citing](#citing)
-[License](#license)
If you want to check the latest version you can find in the [convlab3 official repo](https://github.com/ConvLab/ConvLab-3)
## Updates
-**2023.2.26**: Update ConvLab on PyPI to 3.0.1 to reflect bug fixes.
-**2022.11.30**: ConvLab-3 release.
## Installation
You can install ConvLab-3 in one of the following ways according to your need. We use `torch>=1.10.1,<=1.13` and `transformers>=4.17.0,<=4.24.0`. Higher versions of `torch` and `transformers` may also work.
### Git clone and pip install in development mode (Recommend)
For the latest and most configurable version, we recommend installing ConvLab-3 in development mode.
`dataset` can be `emowoz+dialmage`, `emowoz`, or `dialmage`. `emowoz+dialmage` is the whole dataset pf EmoWOZ, which is composed with human-human (`emowoz`) and human-machine (`dialmage`) task-oriented dialogues
`dial_ids_order` can be 0, 1 or 2
`split2ratio` can be 0.01, 0.1 or 1
To use ConvLab-3 as an off-the-shelf tool, you can install via:
The `build_data.py` will generate three files, `train.json`, `validation.json`, and `test.json`, under the folder `convlab/policy/emoUS/unify/data/EmoUS_${dataset}_${dial_ids_order}_${split2ration}`.
We trained EmoUS on A100 or RTX6000.
```bash
pip install convlab
### Evaluate EmoUS
```
Note that the `data` directory will not be included due to the package size limitation.
### Using Docker
We also provide [Dockerfile](https://github.com/ConvLab/ConvLab-3/blob/master/Dockerfile) for building docker. Basically it uses the `requirement.txt` and then installs ConvLab-3 in development mode.
-[Getting Started](https://github.com/ConvLab/ConvLab-3/blob/master/tutorials/Getting_Started.ipynb)(Have a try on [Colab](https://colab.research.google.com/github/ConvLab/ConvLab-3/blob/master/tutorials/Getting_Started.ipynb)!)
-[Introduction to Unified Data Format](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets)
-[Utility functions for unified datasets](https://github.com/ConvLab/ConvLab-3/blob/master/convlab/util/unified_datasets_util.py)
Unified datasets are available under `data/unified_datasets` directory as well as [Hugging Face Hub](https://huggingface.co/ConvLab). We will continue adding more datasets listed in [this issue](https://github.com/ConvLab/ConvLab-3/issues/11). If you want to add a listed/custom dataset to ConvLab-3, you can create an issue for discussion and then create pull-request. We will list you as the [contributors](#Team) and highly appreciate your contributions!
## Models
We list newly integrated models in ConvLab-3 that support unified data format and obtain strong performance. You can follow the link for more details about these models. Other models can be used in the same way as in ConvLab-2.
Trained models are available on [Hugging Face Hub](https://huggingface.co/ConvLab).
## Contributing
We welcome contributions from community. Please see issues to find what we need.
- If you want to add a new dataset, model, or other feature, please describe the dataset/model/feature in an issue with corresponding issue template before creating pull-request.
- Small change like fixing a bug can be directly made by a pull-request.
## Code Structure
```bash
.
├── convlab # Source code, installed in pypi package
│ ├── dialog_agent # Interface for dialog agent and session
│ ├── base_models
│ │ └── t5 # T5 models with a unified training script
│ │ ├── goal2dialogue # T5-Goal2Dialogue
│ │ ├── dst # T5-DST
│ │ ├── nlu # T5-NLU
│ │ ├── nlg # T5-NLG
│ │ └── rg # T5-RG
│ │
│ ├── nlu # NLU models, interface, and evaluation script
│ │ ├── jointBERT # BERTNLU
│ │ ├── milu # MILU
│ │ └── svm # SVMNLU*
│ │
│ ├── laug # Language understanding AUGmentation (LAUG) toolkit
│ │
│ ├── dst # DST models, interface, and evaluation script
│ │ ├── rule # RuleDST
│ │ ├── setsumbt # SetSUMBT, has uncertainty estimates
then you can train your system with this simulator.
*: models do not support unified datasets, only support MultiWOZ.
## Team
**ConvLab-3** is maintained and developed by [Tsinghua University Conversational AI](http://coai.cs.tsinghua.edu.cn/) group (THU-COAI), the [Dialogue Systems and Machine Learning Group](https://www.cs.hhu.de/en/research-groups/dialog-systems-and-machine-learning.html) at Heinrich Heine University, Düsseldorf, Germany and Microsoft Research (MSR).
We would like to thank all contributors of ConvLab:
You can also change the `mode` to `"language"`, then GenTUS will response in natural language instead of semantic actions.
--->
## Citing
If you use ConvLab-3 in your research, please cite:
```
@article{zhu2022convlab3,
title={ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format},
author={Qi Zhu and Christian Geishauser and Hsien-chin Lin and Carel van Niekerk and Baolin Peng and Zheng Zhang and Michael Heck and Nurul Lubis and Dazhen Wan and Xiaochen Zhu and Jianfeng Gao and Milica Gašić and Minlie Huang},
journal={arXiv preprint arXiv:2211.17148},
year={2022},
url={http://arxiv.org/abs/2211.17148}
}
Hsien-Chin Lin, Shutong Feng, Christian Geishauser, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Matthias Ruppik, Renato Vukovic, and Milica Gašić. 2023. EmoUS: Simulating User Emotions in Task-Oriented Dialogues. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), July 23–27, 2023, Taipei, Taiwan. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3539618.3592092
