Initial commit

.gitignore

@@ -0,0 +1,96 @@
+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
+

LICENSE

@@ -0,0 +1,21 @@
+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.

NOTICE

@@ -0,0 +1,18 @@
+// 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 

README.md

@@ -1,4 +1,4 @@
-# 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.
@@ -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
 

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')