OVSlotTagging

Code for EMNLP 2020 paper: Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots.

Requirements

python==3.7.0
pytorch==1.3.1
allennlp==0.9.0

BERT Pretrained Model

The BERT pretrained models is available at https://huggingface.co/models. To run experiments with BERT, you should download the bert-large-uncased model to local file system and modify the path ("pretrained_model": "/local/path/to/bert-large-uncased/") in the config file to your downloading path.

Dataset

The datasets we use are publicly available at:

We provided dataset in /data dir, together with the statistics including:

number of samples for each slot type
list of intents where the slot type appears
example with longest slot value (in number of tokens)
number of tokens for longest slot value
list of slot values
number of slot value types

The original splits for MIT-restaurant only contain train set (7660 samples) and test set (1521 samples). We further split the original train set into train (6894 samples) and valid (766 samples) sets.

Commands

To train the model, run:

python3 train.py --config_path ./config/SETTING.json --output_dir ./outputs/SETTING/

where SETTING should be the specific setting in /config dir, e.g. rnn-snips, bert-mr or your customized configs.

To test the trained model, run:

python3 test.py --output_dir ./outputs/SETTING/

where ./outputs/SETTING/ should be the output_dir param same as that in the training command.

Citation

If you find the code useful, please consider citing our paper:

@inproceedings{yan-etal-2020-adversarial,
    title = "Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots",
    author = "Yan, Yuanmeng  and
      He, Keqing  and
      Xu, Hong  and
      Liu, Sihong  and
      Meng, Fanyu  and
      Hu, Min  and
      Xu, Weiran",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.490",
    pages = "6070--6075",
}

yym6472 / OVSlotTagging