Position-aware Attention RNN Model for Relation Extraction

This repo contains the PyTorch code for paper Position-aware Attention and Supervised Data Improve Slot Filling.

The TACRED dataset: Details on the TAC Relation Extraction Dataset can be found on this dataset website.

Requirements

• Python 3 (tested on 3.6.2)
• PyTorch (tested on 1.0.0)
• unzip, wget (for downloading only)

Preparation

First, download and unzip GloVe vectors from the Stanford website, with:

chmod +x download.sh; ./download.sh

Then prepare vocabulary and initial word vectors with:

python prepare_vocab.py dataset/tacred dataset/vocab --glove_dir dataset/glove

This will write vocabulary and word vectors as a numpy matrix into the dir dataset/vocab.

Training

Train a position-aware attention RNN model with:

python train.py --data_dir dataset/tacred --vocab_dir dataset/vocab --id 00 --info "Position-aware attention model"

Use --topn N to finetune the top N word vectors only. The script will do the preprocessing automatically (word dropout, entity masking, etc.).

Train an LSTM model with:

python train.py --data_dir dataset/tacred --vocab_dir dataset/vocab --no-attn --id 01 --info "LSTM model"

Model checkpoints and logs will be saved to ./saved_models/00.

Evaluation

Run evaluation on the test set with:

python eval.py saved_models/00 --dataset test

This will use the best_model.pt by default. Use --model checkpoint_epoch_10.pt to specify a model checkpoint file. Add --out saved_models/out/test1.pkl to write model probability output to files (for ensemble, etc.).

Ensemble

Please see the example script ensemble.sh.

License

All work contained in this package is licensed under the Apache License, Version 2.0. See the included LICENSE file.