Source code for LREC-COLING 2024 paper titled "Zero-shot Event Detection using a Textual Entailment Model as an Enhanced Annotator".
Figure 1: The illustration of the difference between a textual entailment model as a frozen annotator and an enhanced annotator.
Figure 2: The general workflow of using a pre-trained TE model and keyword expansion to annotate unlabeled data.
- We propose to use a TE(Textual Entailment) model to annotate large-scale unlabeled text and use annotated data to finetune the TE model, yielding an improved TE model.
- We propose to use keywords to filter out sentences with a low probability of expressing event(s).
- We expand limited number of seed keywords using WordNet, so that we can use the TE model to annotate unlabeled text efficiently.
- The experimental results show that our method can outperform other baselines by 15% on the ACE05 dataset.
Install the necessary packages with:
$ pip install -r requirements.txt
Related data resources can be downloaded from Google Drive.
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To finetune the TE model
cd ZS_TE/train_te_model You may modify the arguments in
fine_tune.py
python fine_tune.py -
To do the event detection task
cd ZS_TE/predict_event You may modify the arguments in
pred(smoothing).py
python pred(smoothing).py -
To do the trigger classification task
cd ZS_TE/bert_classification You may modify the arguments in
bert_c.pyanddataset.py
python bert_c.py