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[EMNLP 2018] Towards Universal Dialogue State Tracking

License: Other

Python 100.00%

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liuchang97 dantodor wangdf62 yuntaow 90217 nangeblog ehwa009 sun-zhen zhang-shubo vaibhavqwerty guillermogsjc

statenet's Issues

请教如何复现模型

作者大大好，我直接下载了整个项目，运行get_embedding.py获取到embed_vN3.npy词向量文件文件后，执行mat_data.py获取了test_nbest.json、test_nbest_tagged.json、train_nbest.json、train_nbest_tagged.json、dev_nbest.json、dev_nbest_tagged.json后，运行offline_model_dstc中的函数train_dstc2(1)，更改设置了变量ctx='cpu'，但运行多个epoch后发现评价指标并没有发生变化，以下为运行的部分输出：

我现在有两个请求，如果能够得到作者的回应对我的研究有莫大的帮助！如下
第一，能简述下复现模型的步骤么？（先执行哪一个文件后执行哪一个文件）
二，能简述下项目中数据预处理的文件么？（数据预处理的大致功能效果）
不论是否能够得到解答，感谢作者的代码贡献和分享！

Thnks for sharing the repo. Does this repo works for WOZ 2.0 dataset as well?

关于2-norm distance

为什用$p_s(v_i) = Softmax(-|| o_s - v_i||) $ 而不用$p_s(v_i) = Softmax(o_s^T v_i) $？

有数学上还是实验结果的原因吗？

Error when running.

{'goal_food': 93, 'requested': 8, 'goal_name': 115, 'goal_area': 7, 'goal_pricerange': 5, 'method': 5}
[INFO]: nn_type is doublelstm, buckets are [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]
Traceback (most recent call last):
  File "offline_model_dstc.py", line 268, in <module>
    train_dstc2(0)
  File "offline_model_dstc.py", line 108, in train_dstc2
    tmp_model = OfflineModel(i, ini, ctx, offline_config_dict)
  File "/net/callisto/storage3/naru/Repos/DST/statenet/offline_model.py", line 110, in __init__
    self.lectrack = LecTrack(lectrack_config_dict)
  File "/net/callisto/storage3/naru/Repos/DST/statenet/lectrack.py", line 189, in __init__
    self.model.bind(data_shapes = self.default_provide_data, label_shapes = self.default_provide_label)
  File "/net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/module/bucketing_module.py", line 343, in bind
    force_rebind=False, shared_module=None, grad_req=grad_req)
  File "/net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/module/module.py", line 429, in bind
    state_names=self._state_names)
  File "/net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/module/executor_group.py", line 279, in __init__
    self.bind_exec(data_shapes, label_shapes, shared_group)
  File "/net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/module/executor_group.py", line 375, in bind_exec
    shared_group))
  File "/net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/module/executor_group.py", line 662, in _bind_ith_exec
    shared_buffer=shared_data_arrays, **input_shapes)
  File "/net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/symbol/symbol.py", line 1529, in simple_bind
    raise RuntimeError(error_msg)
RuntimeError: simple_bind error. Arguments:
slot: (32, 3, 300)
value_1: (32, 5, 300)
softmax_label: (32, 30, 3)
value_0: (32, 93, 300)
value_2: (32, 7, 300)
data_act: (32, 30, 1273)
backward_l0_init_c: (32, 128)
backward_l0_init_h: (32, 128)
data: (32, 30, 30, 300)
[10:58:52] src/storage/storage.cc:116: Compile with USE_CUDA=1 to enable GPU usage

Stack trace returned 10 entries:
[bt] (0) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x23d55a) [0x7f512588555a]
[bt] (1) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x23dbc1) [0x7f5125885bc1]
[bt] (2) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x32aa805) [0x7f51288f2805]
[bt] (3) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x32af1df) [0x7f51288f71df]
[bt] (4) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x32b0682) [0x7f51288f8682]
[bt] (5) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::common::InitZeros(mxnet::NDArrayStorageType, nnvm::TShape const&, mxnet::Context const&, int)+0xa9b) [0x7f51281f35cb]
[bt] (6) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::common::ReshapeOrCreate(std::string const&, nnvm::TShape const&, int, mxnet::NDArrayStorageType, mxnet::Context const&, std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, mxnet::NDArray> > >*, bool)+0x96f) [0x7f512820090f]
[bt] (7) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::exec::GraphExecutor::InitArguments(nnvm::IndexedGraph const&, std::vector<nnvm::TShape, std::allocator<nnvm::TShape> > const&, std::vector<int, std::allocator<int> > const&, std::vector<int, std::allocator<int> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::unordered_set<std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::string> > const&, mxnet::Executor const*, std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, mxnet::NDArray> > >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*)+0xab0) [0x7f5128207d50]
[bt] (8) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::exec::GraphExecutor::Init(nnvm::Symbol, mxnet::Context const&, std::map<std::string, mxnet::Context, std::less<std::string>, std::allocator<std::pair<std::string const, mxnet::Context> > > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::unordered_map<std::string, nnvm::TShape, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, nnvm::TShape> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::unordered_set<std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::string> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, mxnet::NDArray> > >*, mxnet::Executor*, std::unordered_map<nnvm::NodeEntry, mxnet::NDArray, nnvm::NodeEntryHash, nnvm::NodeEntryEqual, std::allocator<std::pair<nnvm::NodeEntry const, mxnet::NDArray> > > const&)+0x76d) [0x7f512821628d]
[bt] (9) /net/callisto/storage3/naru/anaconda3/envs/statenet/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::Executor::SimpleBind(nnvm::Symbol, mxnet::Context const&, std::map<std::string, mxnet::Context, std::less<std::string>, std::allocator<std::pair<std::string const, mxnet::Context> > > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::unordered_map<std::string, nnvm::TShape, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, nnvm::TShape> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::unordered_set<std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::string> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, mxnet::NDArray> > >*, mxnet::Executor*)+0x1d1) [0x7f51282183f1]

Any solutions?

Reproducing the results for DSTC2

Hi,
I am unsure how to reproduce the results reported on the paper. I have generated the embeddings with get_embedding.py and the train_nbest.json and test_nbest.json with mat_data.py. To train, I am running the code offline_model_dstc.py but the accuracy is not improving. Am I missing something?

=============BEGIN EPOCH 0===================
('customAcc', 0.3751827485380117)
[Training over]  2019-06-24 17-08
train: [('customAcc', 0.0)]
dev  : [('customAcc', 0.0)]
test : [('customAcc', 0.0)]
[Testing over]  2019-06-24 17-11
devbest epoch: -1, acc: 0.0
testbest epoch: -1, acc: 0.0
=============BEGIN EPOCH 1===================
('customAcc', 0.32132292781169364)
[Training over]  2019-06-24 17-12
train: [('customAcc', 0.0)]
dev  : [('customAcc', 0.0)]
test : [('customAcc', 0.0)]
[Testing over]  2019-06-24 17-15
devbest epoch: -1, acc: 0.0
testbest epoch: -1, acc: 0.0
=============BEGIN EPOCH 2===================
('customAcc', 0.32132292781169364)
[Training over]  2019-06-24 17-17
train: [('customAcc', 0.0)]
dev  : [('customAcc', 0.0)]
test : [('customAcc', 0.0)]
[Testing over]  2019-06-24 17-20
devbest epoch: -1, acc: 0.0
testbest epoch: -1, acc: 0.0
=============BEGIN EPOCH 3===================
('customAcc', 0.32132292781169364)
[Training over]  2019-06-24 17-22
train: [('customAcc', 0.0)]
dev  : [('customAcc', 0.0)]
test : [('customAcc', 0.0)]
[Testing over]  2019-06-24 17-24
devbest epoch: -1, acc: 0.0
testbest epoch: -1, acc: 0.0

where the 'vocab.dict'?

Thanks for you code.
Could you release the 'vocab.dict' file, which needed in data.py, I can't find it in the source. And the json files 'train_nbest.json' ,'train_nbest_tagged.json' are created by data.py?

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