精读+实现论文《TRADE》

论文：《Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems》

源码：https://github.com/jasonwu0731/trade-dst#zero-shot-dst

参考：https://www.jianshu.com/p/fcaf02fc025d

https://blog.csdn.net/weixin_44385551/article/details/103673408

这是一篇迁移学习+多领域对话系统的论文。

一、训练过程

首先，笔者使用colab进行训练，设置运行时类型，只能用GPU，不能用TPU。估计跟代码有关。

需要看：原文4.2 Training Details 和 4.3 Results以及github作者说明。

训练时只用到(restaurant, hotel, attraction, taxi, train) 五个领域，因为另外两个(hospital, police) 对话太少了。

这里有两个评价指标，共同目标准确率(joint goal accuracy)和槽准确率(slot accuracy)。前者要求DST输出的第t轮对话状态(dialogue state)Bt=(领域，槽，槽值)和标签完全一致，即这一轮中每一个槽值都预测正确才算这一轮正确，而后者只计算(领域，槽，槽值)三元组的准确率。

1.Multi-Domain DST（Multi-domain Joint Training）

Multi-domain Joint Training：优化器选择Adam，batchsize为32，学习率[0.001,0.0001] 0.2的衰减，公式(7)中权重超参都设置为1，嵌入向量由拼接 Glove(Pennington et al., 2014)和单词级嵌入(Hashimoto et al., 2016)初始化嵌入维度为400。解码器用了贪婪搜索(注sequence2sequence的输出Beam Search用的比较多)。同时为了加强模型泛化能力 utterance encoder 加入了word dropout(和drop out类似随机屏蔽掉几个词，出处 Bowman et al. (2016))。

（1）命令

Training

❱❱❱ python3 myTrain.py -dec=TRADE -bsz=32 -dr=0.2 -lr=0.001 -le=1
eg:python3 myTrain.py -dec=TRADE -bsz=32 -dr=0.2 -lr=0.001 -le=1 --parallel_decode=1

Testing

❱❱❱ python3 myTest.py -path=${save_path}

eg:python3 myTest.py -path=

参数说明：

-bsz: batch size

-dr: drop out ratio

-lr: learning rate

-le: loading pretrained embeddings

-path: model saved path

--parallel_decode=1：speedup decoding process

注意：这里的--parallel_decode=1只能用在Multi-Domain DST的training，下面的Unseen Domain DST不能用！！！

（2）结果

本文和四个base-line做了比较， MDBT (Ramadan et al., 2018), GLAD (Zhong et al., 2018), GCE (Nouri and Hosseini-Asl, 2018), 和 SpanPtr (Xu and Hu, 2018)，结果如下：

结果表明TRADE在Multi-domain Joint Training情况下，效果比其他几个都要好

2.Unseen Domain DST（Domain Expanding）

2.1 Zero-Shot DST

（1）命令

Training

❱❱❱ python3 myTrain.py -dec=TRADE -bsz=32 -dr=0.2 -lr=0.001 -le=1 -exceptd=${domain}
eg:python3 myTrain.py -dec=TRADE -bsz=32 -dr=0.2 -lr=0.001 -le=1 -exceptd=attraction

#训练时候，设置-path，可以从上次终端之前最新保存的模型开始训练。【这里的训练是下面few-shot的基础，但是这里太慢了，是在是艰难！两天才训练了8轮，还总是各种原因中断，就包括我设置了TPU!】
python3 myTrain.py -dec=TRADE -bsz=32 -dr=0.2 -lr=0.001 -le=1 -exceptd=attraction -path='/content/drive/My Drive/AI/trade-dst/save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398'

Testing

❱❱❱ python3 myTest.py -path=${save_path} -exceptd=${domain}
eg:python3 myTest.py -path='save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398' -exceptd=attraction
注意：这里的-path，不能用绝对路径，否则会出错，下面同理。

测试输出：
/content/drive/My Drive/AI/trade-dst
{'dataset': 'multiwoz', 'task': 'dst', 'path': 'save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398', 'sample': None, 'patience': 6, 'earlyStop': 'BLEU', 'all_vocab': 1, 'imbalance_sampler': 0, 'data_ratio': 100, 'unk_mask': 1, 'batch': None, 'run_dev_testing': 0, 'vizualization': 0, 'genSample': 0, 'evalp': 1, 'addName': 'Exceptattraction', 'eval_batch': 0, 'use_gate': 1, 'load_embedding': 0, 'fix_embedding': 0, 'parallel_decode': 0, 'decoder': None, 'hidden': 400, 'learn': None, 'drop': None, 'limit': -10000, 'clip': 10, 'teacher_forcing_ratio': 0.5, 'lambda_ewc': 0.01, 'fisher_sample': 0, 'all_model': False, 'domain_as_task': False, 'run_except_4d': 1, 'strict_domain': False, 'except_domain': 'attraction', 'only_domain': ''}
['save', 'TRADE-Exceptattractionmultiwozdst', 'HDD400BSZ32DR0.2ACC-0.5398']
HDD 400 decoder TRADE BSZ 32
False
folder_name save/
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Read 0 pairs train
Read 7331 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 0
Vocab_size Belief 983
Max. length of dialog words for RNN: 660 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 27 in total
['hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-book stay', 'hotel-book day', 'hotel-book people', 'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', 'train-book people', 'train-leaveat', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', 'restaurant-name', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', 'restaurant-book time', 'restaurant-book day', 'restaurant-book people', 'taxi-arriveby']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
MODEL save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398 LOADED
Test Set on 4 domains...
STARTING EVALUATION
100% 229/229 [04:23<00:00,  1.13s/it]
{'Joint Acc': 0.5392491467576792, 'Turn Acc': 0.9687422576159594, 'Joint F1': 0.8946688407302734}
Test Set ...
STARTING EVALUATION
100% 98/98 [00:14<00:00,  6.94it/s]
{'Joint Acc': 0.19581993569131834, 'Turn Acc': 0.5506966773847822, 'Joint F1': 0.20336548767416934}

参数说明：

-exceptd: except domain selection, choose one from {hotel, train, attraction, restaurant, taxi}.

（2）结果

可以看到只有taxi领域的Zero-shot最接近左列，作者的解释是因为taxi领域的四个槽槽值都相似。

2.2 Few-Shot DST with CL（Expanding DST for Few-shot Domain）

（1）命令

Training Naive（重新训练，training 1% new domain）

❱❱❱ python3 fine_tune.py -bsz=8 -dr=0.2 -lr=0.001 -path=${save_path_except_domain} -exceptd=${except_domain}
eg:python3 fine_tune.py -bsz=8 -dr=0.2 -lr=0.001 -path='save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398' -exceptd=attractio

训练输出：
{'dataset': 'multiwoz', 'task': 'dst', 'path': 'save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398', 'sample': None, 'patience': 6, 'earlyStop': 'BLEU', 'all_vocab': 1, 'imbalance_sampler': 0, 'data_ratio': 100, 'unk_mask': 1, 'batch': 8, 'run_dev_testing': 0, 'vizualization': 0, 'genSample': 0, 'evalp': 1, 'addName': 'Exceptattraction', 'eval_batch': 0, 'use_gate': 1, 'load_embedding': 0, 'fix_embedding': 0, 'parallel_decode': 0, 'decoder': None, 'hidden': 400, 'learn': 0.001, 'drop': 0.2, 'limit': -10000, 'clip': 10, 'teacher_forcing_ratio': 0.5, 'lambda_ewc': 0.01, 'fisher_sample': 0, 'all_model': False, 'domain_as_task': False, 'run_except_4d': 1, 'strict_domain': False, 'except_domain': 'attraction', 'only_domain': ''}
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 3381, 'train': 3103, 'attraction': 2717, 'restaurant': 3813, 'taxi': 1654}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Read 56177 pairs train
Read 7331 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 1005
Max. length of dialog words for RNN: 880 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 27 in total
['hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-book stay', 'hotel-book day', 'hotel-book people', 'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', 'train-book people', 'train-leaveat', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', 'restaurant-name', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', 'restaurant-book time', 'restaurant-book day', 'restaurant-book people', 'taxi-arriveby']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 33, 'train': 35, 'restaurant': 36, 'attraction': 29, 'taxi': 11}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Read 232 pairs train
Read 3088 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 983
Max. length of dialog words for RNN: 660 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:50: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1
  "num_layers={}".format(dropout, num_layers))
MODEL save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398 LOADED
Epoch:0
L:1.82,LP:1.11,LG:0.71: 100% 29/29 [00:01<00:00, 15.90it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.41it/s]
{'Joint Acc': 0.3248056994818653, 'Turn Acc': 0.6430267702936108, 'Joint F1': 0.4696891191709857}
MODEL SAVED
Epoch:1
L:0.54,LP:0.27,LG:0.27: 100% 29/29 [00:01<00:00, 15.08it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.25it/s]
{'Joint Acc': 0.37176165803108807, 'Turn Acc': 0.6709844559585499, 'Joint F1': 0.5357620898100195}
MODEL SAVED
Epoch:2
L:0.31,LP:0.16,LG:0.16: 100% 29/29 [00:01<00:00, 15.61it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.25it/s]
{'Joint Acc': 0.37370466321243523, 'Turn Acc': 0.6797279792746118, 'Joint F1': 0.5280332469775502}
MODEL SAVED
Epoch:3
L:0.24,LP:0.15,LG:0.10: 100% 29/29 [00:01<00:00, 15.36it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.06it/s]
{'Joint Acc': 0.3636658031088083, 'Turn Acc': 0.6693652849740939, 'Joint F1': 0.5185772884283271}
Epoch:4
L:0.21,LP:0.10,LG:0.11: 100% 29/29 [00:01<00:00, 15.36it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.40it/s]
{'Joint Acc': 0.36075129533678757, 'Turn Acc': 0.6716321243523327, 'Joint F1': 0.531012521588949}
Epoch     5: reducing learning rate of group 0 to 5.0000e-04.
Epoch:5
L:0.15,LP:0.07,LG:0.08: 100% 29/29 [00:01<00:00, 15.16it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.34it/s]
{'Joint Acc': 0.37208549222797926, 'Turn Acc': 0.682210708117445, 'Joint F1': 0.5309693436960298}
Epoch:6
L:0.14,LP:0.07,LG:0.07: 100% 29/29 [00:01<00:00, 15.48it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.00it/s]
{'Joint Acc': 0.3873056994818653, 'Turn Acc': 0.6889032815198624, 'Joint F1': 0.5552677029360991}
MODEL SAVED
Epoch:7
L:0.15,LP:0.08,LG:0.07: 100% 29/29 [00:01<00:00, 15.43it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.38it/s]
{'Joint Acc': 0.3604274611398964, 'Turn Acc': 0.6707685664939559, 'Joint F1': 0.5084628670120919}
Epoch:8
L:0.09,LP:0.04,LG:0.05: 100% 29/29 [00:01<00:00, 15.52it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.48it/s]
{'Joint Acc': 0.3707901554404145, 'Turn Acc': 0.6788644214162353, 'Joint F1': 0.5320811744386896}
Epoch     9: reducing learning rate of group 0 to 2.5000e-04.
Epoch:9
L:0.09,LP:0.04,LG:0.04: 100% 29/29 [00:01<00:00, 15.57it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.45it/s]
{'Joint Acc': 0.37694300518134716, 'Turn Acc': 0.68199481865285, 'Joint F1': 0.5321783246977567}
Epoch:10
L:0.10,LP:0.04,LG:0.05: 100% 29/29 [00:01<00:00, 15.63it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 14.88it/s]
{'Joint Acc': 0.3753238341968912, 'Turn Acc': 0.6822107081174438, 'Joint F1': 0.5298035405872213}
Epoch    11: reducing learning rate of group 0 to 1.2500e-04.
Epoch:11
L:0.08,LP:0.04,LG:0.04: 100% 29/29 [00:01<00:00, 16.06it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.45it/s]
{'Joint Acc': 0.3711139896373057, 'Turn Acc': 0.6810233160621771, 'Joint F1': 0.5278065630397256}
Epoch:12
L:0.11,LP:0.05,LG:0.06: 100% 29/29 [00:01<00:00, 15.28it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.35it/s]
{'Joint Acc': 0.3756476683937824, 'Turn Acc': 0.6839378238341981, 'Joint F1': 0.5391731433506065}
Epoch    13: reducing learning rate of group 0 to 1.0000e-04.
Ran out of patient, early stop...
[Info] After Fine Tune ...
[Info] Test Set on 4 domains...
STARTING EVALUATION
100% 916/916 [07:43<00:00,  2.10it/s]
{'Joint Acc': 0.3971331058020478, 'Turn Acc': 0.9562432056629776, 'Joint F1': 0.8310067907423058}
[Info] Test Set on 1 domain attraction ...
STARTING EVALUATION
100% 389/389 [00:25<00:00, 15.38it/s]
{'Joint Acc': 0.367524115755627, 'Turn Acc': 0.6797427652733147, 'Joint F1': 0.5233440514469476}

EWC（微调1：elastic weight consolidation）

❱❱❱ python3 EWC_train.py -bsz=8 -dr=0.2 -lr=0.001 -path=${save_path_except_domain} -exceptd=${except_domain} -fisher_sample=10000 -l_ewc=${lambda}
eg:python3 EWC_train.py -bsz=8 -dr=0.2 -lr=0.001 -path='save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398' -exceptd=attraction -fisher_sample=10000

注意：这里的-l_ewc=${lambda}不知道怎么设置，论文和github上面都没有写，就先删掉了，应该有默认值。

微调1训练,第一次运行产生Fisher矩阵，输出：
{'dataset': 'multiwoz', 'task': 'dst', 'path': 'save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398', 'sample': None, 'patience': 6, 'earlyStop': 'BLEU', 'all_vocab': 1, 'imbalance_sampler': 0, 'data_ratio': 100, 'unk_mask': 1, 'batch': 8, 'run_dev_testing': 0, 'vizualization': 0, 'genSample': 0, 'evalp': 1, 'addName': 'Exceptattraction', 'eval_batch': 0, 'use_gate': 1, 'load_embedding': 0, 'fix_embedding': 0, 'parallel_decode': 0, 'decoder': None, 'hidden': 400, 'learn': 0.001, 'drop': 0.2, 'limit': -10000, 'clip': 10, 'teacher_forcing_ratio': 0.5, 'lambda_ewc': 0.01, 'fisher_sample': 10000, 'all_model': False, 'domain_as_task': False, 'run_except_4d': 1, 'strict_domain': False, 'except_domain': 'attraction', 'only_domain': ''}
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 3381, 'train': 3103, 'attraction': 2717, 'restaurant': 3813, 'taxi': 1654}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Read 56177 pairs train
Read 7331 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 1005
Max. length of dialog words for RNN: 880 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 27 in total
['hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-book stay', 'hotel-book day', 'hotel-book people', 'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', 'train-book people', 'train-leaveat', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', 'restaurant-name', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', 'restaurant-book time', 'restaurant-book day', 'restaurant-book people', 'taxi-arriveby']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:50: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1
  "num_layers={}".format(dropout, num_layers))
MODEL save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398 LOADED
Computing Fisher Matrix 
100% 10000/10000 [28:55<00:00,  6.65it/s]
Saving Fisher Matrix in  save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398fisher10000
/usr/local/lib/python3.6/dist-packages/torch/storage.py:34: FutureWarning: pickle support for Storage will be removed in 1.5. Use `torch.save` instead
  warnings.warn("pickle support for Storage will be removed in 1.5. Use `torch.save` instead", FutureWarning)

微调1训练,第二次运行计算评价指标，输出：

/content/drive/My Drive/AI/trade-dst
{'dataset': 'multiwoz', 'task': 'dst', 'path': 'save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398', 'sample': None, 'patience': 6, 'earlyStop': 'BLEU', 'all_vocab': 1, 'imbalance_sampler': 0, 'data_ratio': 100, 'unk_mask': 1, 'batch': 8, 'run_dev_testing': 0, 'vizualization': 0, 'genSample': 0, 'evalp': 1, 'addName': 'Exceptattraction', 'eval_batch': 0, 'use_gate': 1, 'load_embedding': 0, 'fix_embedding': 0, 'parallel_decode': 0, 'decoder': None, 'hidden': 400, 'learn': 0.001, 'drop': 0.2, 'limit': -10000, 'clip': 10, 'teacher_forcing_ratio': 0.5, 'lambda_ewc': 0.01, 'fisher_sample': 10000, 'all_model': False, 'domain_as_task': False, 'run_except_4d': 1, 'strict_domain': False, 'except_domain': 'attraction', 'only_domain': ''}
Load Fisher Matrixsave/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398fisher10000
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 3381, 'train': 3103, 'attraction': 2717, 'restaurant': 3813, 'taxi': 1654}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Read 56177 pairs train
Read 7331 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 1005
Max. length of dialog words for RNN: 880 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 27 in total
['hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-book stay', 'hotel-book day', 'hotel-book people', 'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', 'train-book people', 'train-leaveat', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', 'restaurant-name', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', 'restaurant-book time', 'restaurant-book day', 'restaurant-book people', 'taxi-arriveby']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 33, 'train': 35, 'restaurant': 36, 'attraction': 29, 'taxi': 11}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Read 232 pairs train
Read 3088 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 983
Max. length of dialog words for RNN: 660 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:50: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1
  "num_layers={}".format(dropout, num_layers))
MODEL save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398 LOADED
Epoch:0
L:1.87,LP:1.09,LG:0.78: 100% 29/29 [00:02<00:00, 14.12it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.34it/s]
{'Joint Acc': 0.2933937823834197, 'Turn Acc': 0.6260794473229723, 'Joint F1': 0.4239421416234893}
MODEL SAVED
Epoch:1
L:0.54,LP:0.32,LG:0.22: 100% 29/29 [00:02<00:00, 14.35it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 14.60it/s]
{'Joint Acc': 0.36560880829015546, 'Turn Acc': 0.6984024179620065, 'Joint F1': 0.5461139896373092}
MODEL SAVED
Epoch:2
L:0.29,LP:0.17,LG:0.12: 100% 29/29 [00:02<00:00, 14.36it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.76it/s]
{'Joint Acc': 0.38277202072538863, 'Turn Acc': 0.7129749568221083, 'Joint F1': 0.5885470639032855}
MODEL SAVED
Epoch:3
L:0.29,LP:0.16,LG:0.13: 100% 29/29 [00:02<00:00, 14.06it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.69it/s]
{'Joint Acc': 0.41418393782383417, 'Turn Acc': 0.7161053540587244, 'Joint F1': 0.5956066493955128}
MODEL SAVED
Epoch:4
L:0.21,LP:0.11,LG:0.10: 100% 29/29 [00:02<00:00, 14.56it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 15.02it/s]
{'Joint Acc': 0.3873056994818653, 'Turn Acc': 0.7184801381692614, 'Joint F1': 0.6121545768566526}
Epoch:5
L:0.18,LP:0.09,LG:0.09: 100% 29/29 [00:02<00:00, 14.45it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.38it/s]
{'Joint Acc': 0.4119170984455959, 'Turn Acc': 0.7315414507772071, 'Joint F1': 0.6302892918825584}
Epoch     6: reducing learning rate of group 0 to 5.0000e-04.
Epoch:6
L:0.16,LP:0.09,LG:0.07: 100% 29/29 [00:01<00:00, 13.95it/s]
STARTING EVALUATION
100% 386/386 [00:25<00:00, 14.86it/s]
{'Joint Acc': 0.4167746113989637, 'Turn Acc': 0.7354274611398983, 'Joint F1': 0.6218588082901584}
MODEL SAVED
Epoch:7
L:0.11,LP:0.05,LG:0.06: 100% 29/29 [00:02<00:00, 14.16it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 16.08it/s]
{'Joint Acc': 0.4226036269430052, 'Turn Acc': 0.7434153713298817, 'Joint F1': 0.6380505181347186}
MODEL SAVED
Epoch:8
L:0.09,LP:0.05,LG:0.04: 100% 29/29 [00:02<00:00, 14.17it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.79it/s]
{'Joint Acc': 0.4226036269430052, 'Turn Acc': 0.7401770293609702, 'Joint F1': 0.638331174438691}
MODEL SAVED
Epoch:9
L:0.06,LP:0.03,LG:0.03: 100% 29/29 [00:02<00:00, 14.29it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.65it/s]
{'Joint Acc': 0.4339378238341969, 'Turn Acc': 0.7495682210708151, 'Joint F1': 0.6442141623488813}
MODEL SAVED
Epoch:10
L:0.10,LP:0.06,LG:0.04: 100% 29/29 [00:02<00:00, 14.42it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.69it/s]
{'Joint Acc': 0.4319948186528497, 'Turn Acc': 0.7461139896373087, 'Joint F1': 0.6425841968911951}
Epoch:11
L:0.08,LP:0.03,LG:0.04: 100% 29/29 [00:01<00:00, 14.61it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.79it/s]
{'Joint Acc': 0.4274611398963731, 'Turn Acc': 0.7475172711571708, 'Joint F1': 0.6424978411053575}
Epoch    12: reducing learning rate of group 0 to 2.5000e-04.
Epoch:12
L:0.06,LP:0.03,LG:0.03: 100% 29/29 [00:01<00:00, 14.56it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.77it/s]
{'Joint Acc': 0.4261658031088083, 'Turn Acc': 0.7471934369602793, 'Joint F1': 0.6415263385146842}
Epoch:13
L:0.09,LP:0.03,LG:0.06: 100% 29/29 [00:01<00:00, 14.50it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.52it/s]
{'Joint Acc': 0.42487046632124353, 'Turn Acc': 0.7452504317789324, 'Joint F1': 0.6384822970639064}
Epoch    14: reducing learning rate of group 0 to 1.2500e-04.
Epoch:14
L:0.07,LP:0.03,LG:0.03: 100% 29/29 [00:01<00:00, 14.99it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 16.09it/s]
{'Joint Acc': 0.4264896373056995, 'Turn Acc': 0.7464378238341997, 'Joint F1': 0.636798359240072}
Epoch:15
L:0.07,LP:0.04,LG:0.03: 100% 29/29 [00:01<00:00, 14.07it/s]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.46it/s]
{'Joint Acc': 0.43102331606217614, 'Turn Acc': 0.7489205526770323, 'Joint F1': 0.6395941278065662}
Epoch    16: reducing learning rate of group 0 to 1.0000e-04.
Ran out of patient, early stop...
[Info] After Fine Tune ...
[Info] Test Set on 4 domains...
STARTING EVALUATION
100% 916/916 [08:14<00:00,  2.01it/s]
{'Joint Acc': 0.31098976109215015, 'Turn Acc': 0.9461913790923792, 'Joint F1': 0.7731457294688385}
[Info] Test Set on 1 domain attraction ...
STARTING EVALUATION
100% 389/389 [00:26<00:00, 14.88it/s]
{'Joint Acc': 0.4202572347266881, 'Turn Acc': 0.7412647374062197, 'Joint F1': 0.6160342979635625}

GEM（微调2：gradient episodic memory）

❱❱❱ python3 GEM_train.py -bsz=8 -dr=0.2 -lr=0.001 -path={save_path_except_domain} -exceptd=${except_domain}
eg:python3 GEM_train.py -bsz=8 -dr=0.2 -lr=0.001 -path='save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398' -exceptd=attraction

微调2训练输出：
{'dataset': 'multiwoz', 'task': 'dst', 'path': 'save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398', 'sample': None, 'patience': 6, 'earlyStop': 'BLEU', 'all_vocab': 1, 'imbalance_sampler': 0, 'data_ratio': 100, 'unk_mask': 1, 'batch': 8, 'run_dev_testing': 0, 'vizualization': 0, 'genSample': 0, 'evalp': 1, 'addName': 'Exceptattraction', 'eval_batch': 0, 'use_gate': 1, 'load_embedding': 0, 'fix_embedding': 0, 'parallel_decode': 0, 'decoder': None, 'hidden': 400, 'learn': 0.001, 'drop': 0.2, 'limit': -10000, 'clip': 10, 'teacher_forcing_ratio': 0.5, 'lambda_ewc': 0.01, 'fisher_sample': 0, 'all_model': False, 'domain_as_task': False, 'run_except_4d': 1, 'strict_domain': False, 'except_domain': 'attraction', 'only_domain': ''}
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 3381, 'train': 3103, 'attraction': 2717, 'restaurant': 3813, 'taxi': 1654}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Read 56177 pairs train
Read 7331 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 1005
Max. length of dialog words for RNN: 880 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 27 in total
['hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-book stay', 'hotel-book day', 'hotel-book people', 'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', 'train-book people', 'train-leaveat', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', 'restaurant-name', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', 'restaurant-book time', 'restaurant-book day', 'restaurant-book people', 'taxi-arriveby']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 33, 'train': 35, 'restaurant': 36, 'attraction': 29, 'taxi': 11}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
Read 590 pairs train
Read 7331 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 1005
Max. length of dialog words for RNN: 660 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 27 in total
['hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-book stay', 'hotel-book day', 'hotel-book people', 'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', 'train-book people', 'train-leaveat', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', 'restaurant-name', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', 'restaurant-book time', 'restaurant-book day', 'restaurant-book people', 'taxi-arriveby']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
True
folder_name save/
Reading from data/train_dials.json
domain_counter {'hotel': 33, 'train': 35, 'restaurant': 36, 'attraction': 29, 'taxi': 11}
Reading from data/dev_dials.json
domain_counter {'hotel': 416, 'train': 484, 'attraction': 401, 'restaurant': 438, 'taxi': 207}
Reading from data/test_dials.json
domain_counter {'taxi': 195, 'attraction': 395, 'restaurant': 437, 'train': 494, 'hotel': 394}
[Info] Loading saved lang files...
Read 232 pairs train
Read 3088 pairs dev
Read 3110 pairs test
Vocab_size: 18311 
Vocab_size Training 15462
Vocab_size Belief 983
Max. length of dialog words for RNN: 660 
USE_CUDA=True
[Train Set & Dev Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
[Test Set Slots]: Number is 3 in total
['attraction-area', 'attraction-name', 'attraction-type']
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:50: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1
  "num_layers={}".format(dropout, num_layers))
MODEL save/TRADE-Exceptattractionmultiwozdst/HDD400BSZ32DR0.2ACC-0.5398 LOADED
4 domains test set length used EVAL 7328
4 domains train set length used for GEM 640
1 domains train set length 232
Epoch:0
L:0.89,LP:0.32,LG:0.57: 100% 29/29 [01:16<00:00,  2.68s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.36it/s]
{'Joint Acc': 0.29436528497409326, 'Turn Acc': 0.6319084628670144, 'Joint F1': 0.4437284110535409}
MODEL SAVED
Epoch:1
L:0.52,LP:0.36,LG:0.17: 100% 29/29 [01:20<00:00,  2.79s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.44it/s]
{'Joint Acc': 0.36690414507772023, 'Turn Acc': 0.6906303972366185, 'Joint F1': 0.5358592400690866}
MODEL SAVED
Epoch:2
L:0.20,LP:0.11,LG:0.09: 100% 29/29 [01:18<00:00,  2.62s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.42it/s]
{'Joint Acc': 0.38309585492227977, 'Turn Acc': 0.6974309153713338, 'Joint F1': 0.5791342832469792}
MODEL SAVED
Epoch:3
L:0.13,LP:0.08,LG:0.04: 100% 29/29 [01:19<00:00,  2.67s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.18it/s]
{'Joint Acc': 0.43879533678756477, 'Turn Acc': 0.7376943005181376, 'Joint F1': 0.6300194300518157}
MODEL SAVED
Epoch:4
L:0.26,LP:0.04,LG:0.22: 100% 29/29 [01:17<00:00,  2.70s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.70it/s]
{'Joint Acc': 0.4417098445595855, 'Turn Acc': 0.747301381692576, 'Joint F1': 0.637435233160625}
MODEL SAVED
Epoch:5
L:0.12,LP:0.09,LG:0.03: 100% 29/29 [01:21<00:00,  2.87s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.49it/s]
{'Joint Acc': 0.42843264248704666, 'Turn Acc': 0.7400690846286737, 'Joint F1': 0.6442573402417988}
Epoch:6
L:0.29,LP:0.06,LG:0.23: 100% 29/29 [01:17<00:00,  2.64s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.64it/s]
{'Joint Acc': 0.42357512953367876, 'Turn Acc': 0.7339162348877418, 'Joint F1': 0.6311420552677062}
Epoch     7: reducing learning rate of group 0 to 5.0000e-04.
Epoch:7
L:0.03,LP:0.01,LG:0.01: 100% 29/29 [01:17<00:00,  2.56s/it]
STARTING EVALUATION
100% 386/386 [00:24<00:00, 15.45it/s]
{'Joint Acc': 0.4177461139896373, 'Turn Acc': 0.732512953367878, 'Joint F1': 0.6171740069084664}
Ran out of patient, early stop...
[Info] After Fine Tune ...
[Info] Test Set on 4 domains...
STARTING EVALUATION
100% 916/916 [07:39<00:00,  2.09it/s]
{'Joint Acc': 0.46484641638225255, 'Turn Acc': 0.9617039565162195, 'Joint F1': 0.8624498952934783}
[Info] Test Set on 1 domain attraction ...
STARTING EVALUATION
100% 389/389 [00:25<00:00, 15.56it/s]
{'Joint Acc': 0.4112540192926045, 'Turn Acc': 0.7296891747052539, 'Joint F1': 0.6005466237942153}

（2）结果

可以看到fine-tune的方式，往往比重新训练(training 1% new domain)效果好。

二、术语解释

1.Fisher information matrix

参考：

https://www.cnblogs.com/DDJ-XLG/p/4943236.html

https://blog.csdn.net/GarfieldEr007/article/details/86773096

posted on 2020-06-16 01:31 西伯尔阅读(671) 评论(0) 编辑收藏举报

精读+实现 论文《TRADE》

一、训练过程

1.Multi-Domain DST（Multi-domain Joint Training）

（1）命令

（2）结果

2.Unseen Domain DST（Domain Expanding）

2.1 Zero-Shot DST

（1）命令

（2）结果

2.2 Few-Shot DST with CL（Expanding DST for Few-shot Domain）

（1）命令

（2）结果

二、术语解释

1.Fisher information matrix

精读+实现论文《TRADE》