Few-shot Learning for Event Detection

This is an official repo for papers:

Learning Prototype Representations Across Few-Shot Tasks for Event Detection

Extensively Matching for Few-shot Learning Event Detection

Prepare data

An event in json format has the following attributes:

preprocess/rams_utils.py generate a list of positive training example

   train.json:
   
   event = {
               'id': '{}#{}'.format(doc_id, trigger_id),
               'token': tokens,    # List of tokens
               'trigger': [trigger_index_start, trigger_index_end],
               'label': label,
               'argument': arguments
           }

preprocess/negative.py generates negative examples from positive examples

train.json -> train.negative.json

preprocess/graph.py run tokenizer, dependency parser and save to .parse file

train.json -> train.parse.json
train.negative.json -> train.negative.parse.json

preprocess/prune.py prune dependency tree and save to .prune file

train.parse.json -> train.prune.json
train.negative.parse.json -> train.negative.prune.json

preprocess/tokenizer.py run BERT tokenizer with bert-base-cased as BERT version

train.prune.json -> train.bert-base-cased.json
train.negative.prune.json -> train.negative.bert-base-cased.json

Run most of the FSL model:

python fsl.py --dataset rams -n 5 -k 5 --encoder bertmlp --model proto

Run ProAct model

python melr.py --dataset rams -n 5 -k 5 --encoder bertmlp --model melr

Citatioin:

@inproceedings{lai2021learning,
  title={Learning Prototype Representations Across Few-Shot Tasks for Event Detection},
  author={Lai, Viet and Dernoncourt, Franck and Nguyen, Thien Huu},
  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  pages={5270--5277},
  year={2021}
}

@inproceedings{lai2020extensively,
  title={Extensively Matching for Few-shot Learning Event Detection},
  author={Lai, Viet Dac and Nguyen, Thien Huu and Dernoncourt, Franck},
  booktitle={Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events},
  pages={38--45},
  year={2020}
}