**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)
*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% | 45.1% |
| test | 12.4% | 43.5% |
| human_val | 10.6% | 49.4% |
`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).
The additional code in **ConvLab-2** required for this work is developed and maintained by the Dialogue Systems and Machine Learning (DSML) group at the Heinrich Heine Universität Düsseldorf.
We would like to thank:
Carel van Niekerk, Andrey Malinin, Christian Geishauser, Michael Heck, Hsien-chin Lin, Nurul Lubis, Shutong Feng and Milica Gašić
## Citing
...
...
@@ -265,6 +103,21 @@ If you use ConvLab-2 in your research, please cite:
}
```
```
@inproceedings{vanniekerk2021-uncertainty,
title = "Uncertainty measures in neural belief tracking and the
effects on dialogue policy performance",
author = "van Niekerk, Carel and Malinin, Andrey and Geishauser,
Christian and Heck, Michael and Lin, Hsien-Chin and Lubis,
Nurul and Feng, Shutong and Ga{\v s}i{\'c}, Milica",
