**TUS** is a domain-independent user simulator with transformers for task-oriented dialogue systems. It is based on the [ConvLab-2](https://github.com/thu-coai/ConvLab-2) framework. Therefore, you should follow their instruction to install the package.
**ConvLab-2** is an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of [ConvLab](https://github.com/ConvLab/ConvLab), ConvLab-2 inherits ConvLab's framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. [[paper]](https://arxiv.org/abs/2002.04793)
## Introduction
Our model is a domain-independent user simulator, which means it is not based on any domain-dependent freatures and the output representation is also domain-independent. Therefore, it can easily adapt to a new domain, without additional feature engineering and model retraining.
-[Installation](#installation)
The code of TUS is in `convlab2/policy/tus` and I also create a rule-based DST of user (`convlab2/dst/rule/multiwoz/dst.py`) based on the rule-based DST in `convlab2/dst/rule/multiwoz/dst.py`.
-[Tutorials](#tutorials)
-[Documents](#documents)
-[Models](#models)
-[Supported Datasets](#Supported-Datasets)
-[End-to-end Performance on MultiWOZ](#End-to-end-Performance-on-MultiWOZ)
-[Module Performance on MultiWOZ](#Module-Performance-on-MultiWOZ)
-[Getting Started](https://github.com/thu-coai/ConvLab-2/blob/master/tutorials/Getting_Started.ipynb)(Have a try on [Colab](https://colab.research.google.com/github/thu-coai/ConvLab-2/blob/master/tutorials/Getting_Started.ipynb)!)
-[Add New Model](https://github.com/thu-coai/ConvLab-2/blob/master/tutorials/Add_New_Model.md)
For more details about these models, You can refer to `README.md` under `convlab2/$module/$model/$dataset` dir such as `convlab2/nlu/jointBERT/multiwoz/README.md`.
- We add user dialogue act (*inform*, *request*, *bye*, *greet*, *thank*), remove 5 sessions that have incomplete dialogue act annotation and place it under `data/multiwoz` dir.
- Train/val/test size: 8434/999/1000. Split as original data.
- We offers a rule-based user simulator and a complete set of models for building a pipeline system on the CrossWOZ dataset. We correct few state annotation and place it under `data/crosswoz` dir.
- Train/val/test size: 5012/500/500. Split as original data.
*Notice*: The results are for commits before [`bdc9dba`](https://github.com/thu-coai/ConvLab-2/commit/bdc9dba72c957d97788e533f9458ed03a4b0137b)(inclusive). We will update the results after improving user policy.
We perform end-to-end evaluation (1000 dialogues) on MultiWOZ using the user simulator below (a full example on `tests/test_end2end.py`) :
*Notice*: The results are for commits before [`bdc9dba`](https://github.com/thu-coai/ConvLab-2/commit/bdc9dba72c957d97788e533f9458ed03a4b0137b)(inclusive). We will update the results after improving user policy.
By running `convlab2/policy/evalutate.py --model_name $model`
| | Task Success Rate |
| --------- | ----------------- |
| MLE | 0.56 |
| PG | 0.54 |
| PPO | 0.74 |
| GDPL | 0.58 |
### NLG
By running `convlab2/nlg/evaluate.py MultiWOZ $model sys`
| | corpus BLEU-4 |
| -------- | ------------- |
| Template | 0.3309 |
| SCLSTM | 0.4884 |
## Translation-train SUMBT for cross-lingual DST
### Train
With Convlab-2, you can train SUMBT on a machine-translated dataset like this:
evaluation of our pre-trained models are: (joint acc.)
| type | CrossWOZ-en | MultiWOZ-zh |
| ----- | ----------- | ----------- |
| val | 12.4% | 48.5% |
| test | 12.4% | 46.0% |
| human_val | 10.6% | 47.4% |
`human_val` option will make the model evaluate on the validation set translated by human.
Note: You may want to download pre-traiend BERT models and translation-train SUMBT models provided by us.
Without modifying any code, you could:
- download pre-trained BERT models from:
-[bert-base-uncased](https://huggingface.co/bert-base-uncased) for CrossWOZ-en
-[chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) for MultiWOZ-zh
extract it to `./pre-trained-models`.
- for translation-train SUMBT model:
-[trained on CrossWOZ-en](https://convlab.blob.core.windows.net/convlab-2/crosswoz_en-pytorch_model.bin.zip)
-[trained on MultiWOZ-zh](https://convlab.blob.core.windows.net/convlab-2/multiwoz_zh-pytorch_model.bin.zip)
- Say the data set is CrossWOZ (English), (after extraction) just save the pre-trained model under `./convlab2/dst/sumbt/crosswoz_en/pre-trained` and name it with `pytorch_model.bin`.
## Issues
You are welcome to create an issue if you want to request a feature, report a bug or ask a general question.
## Contributions
We welcome contributions from community.
- If you want to make a big change, we recommend first creating an issue with your design.
- Small contributions can be directly made by a pull request.
- If you like make contributions to our library, see issues to find what we need.
## Team
**ConvLab-2** is maintained and developed by Tsinghua University Conversational AI group (THU-coai) and Microsoft Research (MSR).
If you use ConvLab-2 in your research, please cite:
```
@inproceedings{zhu2020convlab2,
title={ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems},
author={Qi Zhu and Zheng Zhang and Yan Fang and Xiang Li and Ryuichi Takanobu and Jinchao Li and Baolin Peng and Jianfeng Gao and Xiaoyan Zhu and Minlie Huang},
year={2020},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
