Commit b31b5344 authored by Carel van Niekerk's avatar Carel van Niekerk
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Update README.md

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For the code used in our paper navigate to convlab/dst/setsumbt. See below info regarding the use and installation of the convlab environment.
# ConvLab-2
[![Build Status](https://travis-ci.com/thu-coai/ConvLab-2.svg?branch=master)](https://travis-ci.com/thu-coai/ConvLab-2)
**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)
- [Installation](#installation)
......@@ -25,7 +23,7 @@ Require python 3.6.
Clone this repository:
```bash
git clone https://github.com/thu-coai/ConvLab-2.git
git clone https://gitlab.cs.uni-duesseldorf.de/general/dsml/setsumbt-public.git
```
Install ConvLab-2 via pip:
......@@ -50,7 +48,7 @@ Our documents are on https://thu-coai.github.io/ConvLab-2_docs/convlab2.html.
We provide following models:
- NLU: SVMNLU, MILU, BERTNLU
- DST: rule, TRADE, SUMBT
- DST: rule, TRADE, SUMBT, SetSUMBT
- Policy: rule, Imitation, REINFORCE, PPO, GDPL, MDRG, HDSA, LaRL
- Simulator policy: Agenda, VHUS
- NLG: Template, SCLSTM
......@@ -77,172 +75,6 @@ For more details about these models, You can refer to `README.md` under `convla
- Train/val/test size: 5048/234/526. Split as original data.
- LICENSE: Attribution-NonCommercial 4.0 International, url: https://creativecommons.org/licenses/by-nc/4.0/
## End-to-end Performance on MultiWOZ
*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`) :
```python
# BERT nlu trained on sys utterance
user_nlu = BERTNLU(mode='sys', config_file='multiwoz_sys_context.json', model_file='https://convlab.blob.core.windows.net/convlab-2/bert_multiwoz_sys_context.zip')
user_dst = None
user_policy = RulePolicy(character='usr')
user_nlg = TemplateNLG(is_user=True)
user_agent = PipelineAgent(user_nlu, user_dst, user_policy, user_nlg, name='user')
analyzer = Analyzer(user_agent=user_agent, dataset='multiwoz')
set_seed(20200202)
analyzer.comprehensive_analyze(sys_agent=sys_agent, model_name='sys_agent', total_dialog=1000)
```
Main metrics (refer to `convlab2/evaluator/multiwoz_eval.py` for more details):
- Complete: whether complete the goal. Judged by the Agenda policy instead of external evaluator.
- Success: whether all user requests have been informed and the booked entities satisfy the constraints.
- Book: how many the booked entities satisfy the user constraints.
- Inform Precision/Recall/F1: how many user requests have been informed.
- Turn(succ/all): average turn number for successful/all dialogues.
Performance (the first row is the default config for each module. Empty entries are set to default config.):
| NLU | DST | Policy | NLG | Complete rate | Success rate | Book rate | Inform P/R/F1 | Turn(succ/all) |
| ----------- | --------- | -------------- | ----------- | ------------- | ------------ | --------- | --------- | -------------- |
| **BERTNLU** | RuleDST | RulePolicy | TemplateNLG | 90.5 | 81.3 | 91.1 | 79.7/92.6/83.5 | 11.6/12.3 |
| **MILU** | RuleDST | RulePolicy | TemplateNLG | 93.3 | 81.8 | 93.0 | 80.4/94.7/84.8 | 11.3/12.1 |
| BERTNLU | RuleDST | RulePolicy | **SCLSTM** | 48.5 | 40.2 | 56.9 | 62.3/62.5/58.7 | 11.9/27.1 |
| BERTNLU | RuleDST | **MLEPolicy** | TemplateNLG | 42.7 | 35.9 | 17.6 | 62.8/69.8/62.9 | 12.1/24.1 |
| BERTNLU | RuleDST | **PGPolicy** | TemplateNLG | 37.4 | 31.7 | 17.4 | 57.4/63.7/56.9 | 11.0/25.3 |
| BERTNLU | RuleDST | **PPOPolicy** | TemplateNLG | 61.1 | 44.0 | 44.6 | 63.9/76.8/67.2 | 12.5/20.8 |
| BERTNLU | RuleDST | **GDPLPolicy** | TemplateNLG | 49.4 | 38.4 | 20.1 | 64.5/73.8/65.6 | 11.5/21.3 |
| None | **TRADE** | RulePolicy | TemplateNLG | 32.4 | 20.1 | 34.7 | 46.9/48.5/44.0 | 11.4/23.9 |
| None | **SUMBT** | RulePolicy | TemplateNLG | 34.5 | 29.4 | 62.4 | 54.1/50.3/48.3 | 11.0/28.1 |
| BERTNLU | RuleDST | **MDRG** | None | 21.6 | 17.8 | 31.2 | 39.9/36.3/34.8 | 15.6/30.5|
| BERTNLU | RuleDST | **LaRL** | None | 34.8 | 27.0 | 29.6 | 49.1/53.6/47.8 |13.2/24.4|
| None | **SUMBT** | **LaRL** | None | 32.9 | 23.7 | 25.9 | 48.6/52.0/46.7 | 12.5/24.3|
| None | None | **DAMD*** | None | 39.5| 34.3 | 51.4 | 60.4/59.8/56.3 | 15.8/29.8 |
*: end-to-end models used as sys_agent directly.
## Module Performance on MultiWOZ
### NLU
By running `convlab2/nlu/evaluate.py MultiWOZ $model all`:
| | Precision | Recall | F1 |
| ------- | --------- | ------ | ----- |
| BERTNLU | 82.48 | 85.59 | 84.01 |
| MILU | 80.29 | 83.63 | 81.92 |
| SVMNLU | 74.96 | 50.74 | 60.52 |
### DST
By running `convlab2/dst/evaluate.py MultiWOZ $model`:
| | Joint accuracy | Slot accuracy | Joint F1 |
| -------- | ------------- | ------------- | --------|
| MDBT | 0.06 | 0.89 | 0.43 |
| SUMBT | 0.30 | 0.96 | 0.83 |
| TRADE | 0.40 | 0.96 | 0.84 |
### Policy
*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:
```python
# train.py
import os
from sys import argv
if __name__ == "__main__":
if len(argv) != 2:
print('usage: python3 train.py [dataset]')
exit(1)
assert argv[1] in ['multiwoz', 'crosswoz']
from convlab2.dst.sumbt.multiwoz_zh.sumbt import SUMBT_PATH
if argv[1] == 'multiwoz':
from convlab2.dst.sumbt.multiwoz_zh.sumbt import SUMBTTracker as SUMBT
elif argv[1] == 'crosswoz':
from convlab2.dst.sumbt.crosswoz_en.sumbt import SUMBTTracker as SUMBT
sumbt = SUMBT()
sumbt.train(True)
```
### Evaluate
Execute `evaluate.py` (under `convlab2/dst/`) with following command:
```bash
python3 evaluate.py [CrossWOZ-en|MultiWOZ-zh] [val|test|human_val]
```
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).
......@@ -251,6 +83,12 @@ We would like to thank:
Yan Fang, Zhuoer Feng, Jianfeng Gao, Qihan Guo, Kaili Huang, Minlie Huang, Sungjin Lee, Bing Li, Jinchao Li, Xiang Li, Xiujun Li, Lingxiao Luo, Wenchang Ma, Mehrad Moradshahi, Baolin Peng, Runze Liang, Ryuichi Takanobu, Hongru Wang, Jiaxin Wen, Yaoqin Zhang, Zheng Zhang, Qi Zhu, Xiaoyan Zhu.
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",
year = 2021,
copyright = "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
archivePrefix = "arXiv",
primaryClass = "cs.CL",
eprint = "2109.04349"
}
```
## License
Apache License 2.0
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