xinwei96/coin_dialogre

Source codes and data for our IJCAI 2021 paper "Consistent Inference for Dialogue Relation Extraction".

CoIn_DialogRE

0. Package Description

├─ data/: raw data and preprocessed data
    ├─ train.json
    ├─ dev.json
    ├─ test.json
    ├─ entity_type_id.json
    ├─ speaker_vocab_id.json
    ├─ vocab.txt: bert vocab file, we add the new-introduced special tokens
├─ logs/: save the log files
├─ model/: save the optimal model file and prediction results
├─ src/: source codes
    ├─ attention.py 
    ├─ data_utils.py: utils for processing data
    ├─ dataset.py
    ├─ embeddings.py: generate entity type/ utterance embedding
    ├─ model.py
    ├─ main.py: main file to run the model
├─ readme.md

1. Environments

We conducted experiments on a sever with two GeForce GTX 1080Ti GPU.

• python (3.6.5)
• cuda (11.0)
• CentOS Linux release 7.8.2003 (Core)

2. Dependencies

• torch (1.2.0)
• transformers (2.0.0)
• pytorch-transformers (1.2.0)
• numpy (1.19.2)

3. Preparation

3.1 Download the pre-trained language models.

• Download the bert-base-uncase model.

3.2 Add the special token id into the vocab.txt

• Inspired by the resource paper, we add the newly-introduced special tokens to indicate the speakers. (Replacing [unused1]..[unsued10] with speaker1..speaker10).
• You can replace the original vocab.txt with our file (in './data/vocab.txt')

4. Training

If you want to reproduce our results, please follow our hyper-parameter settings and run the code with the following command.

CUDA_VISIBLE_DEVICES=0,1 nohup python -m torch.distributed.launch --nproc_per_node=2 main.py --bert_path {your_bert_path}

5. Evaluating

You also can evaluate our model without training. Please download the released model. model

python evaluate.py --bert_path {your_bert_path} --optimal_model_path {released_model_path}

Citation

Thank you for your interests in our paper, if you have any problem, please feel free to contact me. ([email protected])

@inproceedings{DBLP:conf/ijcai/LongNL21,
  author    = {Xinwei Long and
               Shuzi Niu and
               Yucheng Li},
  title     = {Consistent Inference for Dialogue Relation Extraction},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial
               Intelligence, {IJCAI} 2021, Virtual Event / Montreal, Canada, 19-27
               August 2021},
  pages     = {3885--3891},
  year      = {2021},
  url       = {https://doi.org/10.24963/ijcai.2021/535}
}

coin_dialogre's Issues

您好，我下载提供的训练好的bert-base-uncased模型，但是在测试集上的F1只有50%

GAIN相关代码

 您好，因为最近刚刚接触关系对话相关任务，对您的工作十分感兴趣。在实验中看到了您使用文档级关系抽取的工作GAIN在对话数据集上，对此十分感兴趣。冒昧提出请求您是否可以分享一下GAIN用在对话数据集上的相关的代码呢？[email protected]
 不甚感激~如果不方便的话那很抱歉打扰了~

请求GAIN、GCGCN代码

你好，请问能否分享论文baseline中用于对话关系抽取的GCGCN和GAIN模型呢，[email protected]，谢谢！

bug请教

感谢开源非常好的工作！
我使用pytorch==1.7.1，两块V100跑这份代码，参数完全按照papers里面的设置，跑后的错误提示应该是分布式的错误，尝试解决没有解决该问题，特来请教！log日志如下：

Namespace(batch_size=2, bert_output_size=768, bert_path='/home/data/bert-base-chinese', dev_path='../data/dev.json', device_name='cuda', dropout_rate=0.2, epoch_nums=60, eval_batch_size=2, ft_lr=2e-05, handle_abbr=True, local_rank=0, log_path='../logs/', lower=True, lr=0.0005, max_input_lens=512, max_seq_lens=725, max_utter_nums=42, mode='train', num_heads=12, offset=256, optimal_model_path='./model/best.pkl', output_path='../model/', preds_output_path='./results.json', rel_nums=36, report_every_batch=50, rule_nums=24, save_dir='../model/', seed=0, sigma=0.1, t_max=10, test_batch_size=1, test_path='../data/test.json', threshold=0.5, train_path='../data/train.json', type_dict_path='../data/entity_type_id.json', weight_decay=1e-07)
Load dialogre dataset... (This can take some time)
loading files...
Load dialogre dataset... (This can take some time)
loading files...
all done.
all done.
Start Training: 1
Traceback (most recent call last):
File "main.py", line 291, in
main()
File "main.py", line 287, in main
train(args)
File "main.py", line 95, in train
preds = model(batch)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 619, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/raid/CoIn_dialogRE-main/src/model.py", line 48, in forward
input_feat, input_mask = self.encoder(input_tokens_1, input_tokens_2, input_mask_1, input_mask_2, batch_size, max_seq_lens)
File "/raid/CoIn_dialogRE-main/src/model.py", line 107, in encoder
input_feat = self.bert_encoder(input_tokens_1, attention_mask=input_mask_1)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
Traceback (most recent call last):
File "main.py", line 291, in
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 710, in forward
main()
File "main.py", line 287, in main
head_mask=head_mask)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
train(args)
File "main.py", line 95, in train
preds = model(batch)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 431, in forward
layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i])
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 619, in forward
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 409, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/raid/CoIn_dialogRE-main/src/model.py", line 48, in forward
input_feat, input_mask = self.encoder(input_tokens_1, input_tokens_2, input_mask_1, input_mask_2, batch_size, max_seq_lens)
File "/raid/CoIn_dialogRE-main/src/model.py", line 114, in encoder
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 366, in forward
input1 = self.bert_encoder(input_tokens_1, attention_mask=input_mask_1)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
self_outputs = self.self(input_tensor, attention_mask, head_mask)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 710, in forward
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 286, in forward
head_mask=head_mask)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
mixed_query_layer = self.query(hidden_states)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 431, in forward
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 93, in forward
return F.linear(input, self.weight, self.bias)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/functional.py", line 1692, in linear
layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i])
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 409, in forward
attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
output = input.matmul(weight.t()) File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl

RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasCreate(handle)
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 366, in forward
self_outputs = self.self(input_tensor, attention_mask, head_mask)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/pytorch_transformers/modeling_bert.py", line 286, in forward
mixed_query_layer = self.query(hidden_states)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, *kwargs)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 93, in forward
return F.linear(input, self.weight, self.bias)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/nn/functional.py", line 1692, in linear
output = input.matmul(weight.t())
RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasCreate(handle)
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: device-side assert triggered
Exception raised from create_event_internal at /opt/conda/conda-bld/pytorch_1607370144807/work/c10/cuda/CUDACachingAllocator.cpp:687 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7fe2eaaa58b2 in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: c10::cuda::CUDACachingAllocator::raw_delete(void) + 0xad2 (0x7fe2eacf7982 in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10::TensorImpl::release_resources() + 0x4d (0x7fe2eaa90b7d in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #3: + 0x5fea0a (0x7fe320aafa0a in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #4: + 0x5feab6 (0x7fe320aafab6 in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: + 0x1a3f6e (0x5563cb729f6e in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #6: + 0x10e34c (0x5563cb69434c in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #7: + 0x10e4a7 (0x5563cb6944a7 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #8: + 0x10e4a7 (0x5563cb6944a7 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #9: + 0xfd9c8 (0x5563cb6839c8 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #10: + 0x10eb77 (0x5563cb694b77 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #11: + 0x10eb8d (0x5563cb694b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #12: + 0x10eb8d (0x5563cb694b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #13: + 0x10eb8d (0x5563cb694b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #14: + 0x10eb8d (0x5563cb694b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #15: PyDict_SetItem + 0x502 (0x5563cb6e9da2 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #16: PyDict_SetItemString + 0x4f (0x5563cb6ea86f in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #17: PyImport_Cleanup + 0xa0 (0x5563cb7305d0 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #18: Py_FinalizeEx + 0x67 (0x5563cb7ab487 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #19: + 0x237f03 (0x5563cb7bdf03 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #20: _Py_UnixMain + 0x3c (0x5563cb7be22c in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #21: __libc_start_main + 0xe7 (0x7fe345776b97 in /lib/x86_64-linux-gnu/libc.so.6)
frame #22: + 0x1dce90 (0x5563cb762e90 in /home/data/miniconda3/envs/DeepEnv/bin/python)

terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: device-side assert triggered
Exception raised from create_event_internal at /opt/conda/conda-bld/pytorch_1607370144807/work/c10/cuda/CUDACachingAllocator.cpp:687 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7efe812068b2 in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: c10::cuda::CUDACachingAllocator::raw_delete(void*) + 0xad2 (0x7efe81458982 in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10::TensorImpl::release_resources() + 0x4d (0x7efe811f1b7d in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #3: + 0x5fea0a (0x7efeb7210a0a in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #4: + 0x5feab6 (0x7efeb7210ab6 in /home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: + 0x1a3f6e (0x55dfc4a9cf6e in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #6: + 0x10e34c (0x55dfc4a0734c in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #7: + 0x10e4a7 (0x55dfc4a074a7 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #8: + 0x10e4a7 (0x55dfc4a074a7 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #9: + 0xfd9c8 (0x55dfc49f69c8 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #10: + 0x10eb77 (0x55dfc4a07b77 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #11: + 0x10eb8d (0x55dfc4a07b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #12: + 0x10eb8d (0x55dfc4a07b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #13: + 0x10eb8d (0x55dfc4a07b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #14: + 0x10eb8d (0x55dfc4a07b8d in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #15: PyDict_SetItem + 0x502 (0x55dfc4a5cda2 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #16: PyDict_SetItemString + 0x4f (0x55dfc4a5d86f in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #17: PyImport_Cleanup + 0xa0 (0x55dfc4aa35d0 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #18: Py_FinalizeEx + 0x67 (0x55dfc4b1e487 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #19: + 0x237f03 (0x55dfc4b30f03 in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #20: _Py_UnixMain + 0x3c (0x55dfc4b3122c in /home/data/miniconda3/envs/DeepEnv/bin/python)
frame #21: __libc_start_main + 0xe7 (0x7efedbed7b97 in /lib/x86_64-linux-gnu/libc.so.6)
frame #22: + 0x1dce90 (0x55dfc4ad5e90 in /home/data/miniconda3/envs/DeepEnv/bin/python)

Traceback (most recent call last):
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/distributed/launch.py", line 260, in
main()
File "/home/data/miniconda3/envs/DeepEnv/lib/python3.7/site-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
subprocess.CalledProcessError: Command '['/home/data/miniconda3/envs/DeepEnv/bin/python', '-u', 'main.py', '--local_rank=1']' died with <Signals.SIGABRT: 6>.

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Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

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您好，请问实验环境使用几张什么配置的显卡？