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jincli committed May 23, 2019
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96 changes: 96 additions & 0 deletions .gitignore
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data/multiwoz/annotation/annotated_user_da_with_span_full.json
data/multiwoz/annotation/MULTIWOZ2 2
tasktk/modules/nlu/SVM/output_multiwoz/
tasktk/modules/nlu/SVM/output_multiwoz_sys/

test_result/
*.pyc
.DS_Store

#Eclipse
*.project

# PyDev specific (Python IDE for Eclipse)
*.pydevproject

.settings/

# pycharm specific
.idea

# vscode
.vscode

# mdbt data
data/mdbt/
data/mdbt.tar.gz
#data/multiwoz/annotated_user_utts_18k.txt
#data/multiwoz/annoted_bio.txt
data/multiwoz/annotated_user_da_with_span_full.json
data/multiwoz/annotated_user_utts_all.txt
data/multiwoz/annotated_qr_pairs.txt
data/multiwoz/annotated_qr_pairs_30k.txt
data/multiwoz/annoted_bio_all_20k.txt
data/multiwoz/annotated_user_utts_v2.txt
data/multiwoz/annotated_user_utts_v2_20k.txt

# private test script
private_test.py

# output of run.py
session.txt

# archives
*.rar

# models
data/models/

ckpts/

# nlu and nlg data
data/nlu
data/nlg
data/nrg

# nrg data
tasktk/modules/nrg/mdrg/data
tasktk/modules/nrg/mdrg/model/model

# sequicity
tasktk/modules/e2e/Sequicity/data
tasktk/modules/e2e/Sequicity/log
tasktk/modules/e2e/Sequicity/models
tasktk/modules/e2e/Sequicity/results
tasktk/modules/e2e/Sequicity/sheets
tasktk/modules/e2e/Sequicity/vocab

# mem2seq
tasktk/modules/e2e/Mem2Seq/data
tasktk/modules/e2e/Mem2Seq/save

# test scripts
interact.py
test_run.py

# mlst
data/multiwoz/mlst.txt
data/multiwoz/sample.json

tasktk/modules/nlu/mlst/models

# joint_nlu
tasktk/modules/nlu/joint_nlu/models
# mle
tasktk/modules/policy/system/vanilla_mle/models

# dst
tasktk/modules/dst/*.log

# rl
temp-plot.html
output

# log
*.log

21 changes: 21 additions & 0 deletions LICENSE
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MIT License

Copyright (c) Microsoft Corporation

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
18 changes: 18 additions & 0 deletions NOTICE
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// Copyright (c) Microsoft Corporation.
// Licensed under the MIT license.


This project may include a number of subcomponents with separate copyright notices
and license terms.

This product includes software from https://github.com/budzianowski/multiwoz under Apache License 2.0

This product includes software from https://github.com/HLTCHKUST/Mem2Seq under MIT License

This product includes software from https://github.com/kengz/SLM-Lab under MIT License

This product includes software from https://github.com/andy194673/nlg-sclstm-multiwoz

This product includes software from https://github.com/osmanio2/multi-domain-belief-tracking

This product includes software from https://github.com/WING-NUS/sequicity
10 changes: 6 additions & 4 deletions README.md
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# ConvLab
# DialogZone
This is a toolkit for developing task-oriented dialog system. We
followed the conventional pipeline framework, where there are 4 seperate
components: NLU, DST, Policy and NLG.
Expand All @@ -17,12 +17,14 @@ system in an RL-based manner.

<!---
- Trained NLG model can be downloaded [here](https://www.dropbox.com/s/7d6rr57hmdcz9pd/lstm_tanh_%5B1549590993.11%5D_24_28_1000_0.447.pkl?dl=0).
-->
-->
- Trained NLG model can be downloaded [here](https://www.dropbox.com/s/u1n8jlgr89jnn2f/lstm_tanh_%5B1552674040.43%5D_7_7_400_0.436.pkl?dl=0).
- Trained NLU model can be downloaded [here](https://www.dropbox.com/s/y2aclsz9t7nmxnr/bi_lstm_%5B1552541377.53%5D_7_7_360_0.912.pkl?dl=0).
- Trained S2S UserSim model can be downloaded [here](https://www.dropbox.com/s/2jxkqp2ad07asps/lstm_%5B1550147645.59%5D_20_29_0.448.p?dl=0).
- Trained MLST NLU model can be downloaded [here](https://1drv.ms/u/s!AmXaP0QRGLFchVZqB047pJdS-tiT). Unzip the downloaded file in the tasktk/nlu/mlst directory.
- Trained JointNLU model can be downloaded [here](https://1drv.ms/u/s!AmXaP0QRGLFchVn7DNj4s7fghLTo). Unzip the downloaded file in the tasktk/nlu/joint_nlu directory.
- Trained MLST NLU model can be downloaded [here](https://1drv.ms/u/s!AmXaP0QRGLFchVtHJ99dYJuRKqE_). Unzip the downloaded file in the tasktk/modules/nlu/mlst directory.
- Trained JointNLU model can be downloaded [here](https://1drv.ms/u/s!AmXaP0QRGLFchVoiN2c1QkvK8vfq). Unzip the downloaded file in the tasktk/modules/nlu/joint_nlu directory.
- Trained SVM NLU model can be downloaded [here](https://drive.google.com/file/d/1y0v0Eq6p2dpVfGzPPeLciOkAkNAvQSqV/view?usp=sharing). Unzip the downloaded file in the tasktk/modules/nlu/SVM/output_multiwoz directory.
- Trained MDRG model can be downloaded [here](https://1drv.ms/u/s!AmXaP0QRGLFchVzGUZIat0-Ym52a). Unzip the downloaded file in the data directory.

## Document

Expand Down
20 changes: 20 additions & 0 deletions convlab/__init__.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

from convlab.modules.usr import UserSimulator
from convlab.modules.nlu.multiwoz import ErrorNLU, JointNLU, MlstNLU, SVMNLU

from convlab.modules.dst.multiwoz import MDBTTracker, RuleDST
from convlab.modules.policy.system.multiwoz import RuleBasedMultiwozBot, RuleInformBot
from convlab.modules.policy.user.multiwoz import UserPolicyAgendaMultiWoz
from convlab.modules.nlg.multiwoz import TemplateNLG, MultiwozTemplateNLG, SCLSTM

from convlab.modules.util import Log

import os

os.environ['PY_ENV'] = os.environ.get('PY_ENV') or 'development'
ROOT_DIR = os.path.normpath(os.path.join(os.path.dirname(__file__), '..'))

EVAL_MODES = ('enjoy', 'eval')
TRAIN_MODES = ('search', 'train', 'dev')
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