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Pytorch Implementation of "Adaptive Co-attention Network for Named Entity Recognition in Tweets" (AAAI 2018)

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NER-Multimodal-pytorch

(Unofficial) Pytorch Implementation of "Adaptive Co-attention Network for Named Entity Recognition in Tweets" (AAAI 2018)

Model

Dependencies

  • python>=3.5
  • torch==1.3.1
  • torchvision==0.4.2
  • pillow==7.0.0
  • pytorch-crf==0.7.2
  • seqeval==0.0.12
  • gdown>=3.10.1
$ pip3 install -r requirements.txt

Data

Train Dev Test
# of Data 4,000 1,000 3,257

1. Pretrained Word Vectors

  • Original code's pretrained word embedding can be downloaded at here.
  • But it takes quite a long time to download, so I take out the word vectors (word_vector_200d.vec) that are only in word vocab.

2. Extracted VGG Features

  • Image features are extracted from last pooling layer of VGG16.

  • If you want to extract the feature by yourself, follow as below.

    1. Clone the repo of original code.
    2. Copy data/ner_img from original code to this repo.
    3. Run as below. (img_vgg_features.pt will be saved in data dir)
    $ python3 save_vgg_feature.py
  • Extracted features will be downloaded automatically when you run main.py.

Detail

  • There are some differences between the paper and the original code, so I tried to follow the paper's equations as possible.
  • Build the vocab with train, dev, and test dataset. (same as the original code)
    • Making the vocab only with train dataset decreases performance a lot. (about 5%)
  • Use Adam optimizer instead of RMSProp.

How to run

$ python3 main.py --do_train --do_eval

Result

F1 (%)
Re-implementation 67.17
Baseline (paper) 70.69

References

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Pytorch Implementation of "Adaptive Co-attention Network for Named Entity Recognition in Tweets" (AAAI 2018)

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