Improving Relation Extraction by Pre-trained Language Representations

This repository contains the code of our paper:
Improving Relation Extraction by Pre-trained Language Representations.
Christoph Alt*, Marc Hübner*, Leonhard Hennig

We fine-tune the pre-trained OpenAI GPT [1] to the task of relation extraction and show that it achieves state-of-the-art results on SemEval 2010 Task 8 and TACRED relation extraction datasets.

Our code depends on huggingface's PyTorch reimplementation of the OpenAI GPT [2] - so thanks to them.

Installation

First, clone the repository to your machine and install the requirements with the following command:

pip install -r requirements.txt

We also need the weights of the pre-trained Transformer, which can be downloaded with the following command:

./download-model.sh

The english spacy model is required for sentence segmentation:

python -m spacy download en

Prepare the data

We evaluate our model on SemEval 2010 Task 8 and TACRED, which is available through LDC.

Our model expects the input dataset to be in JSONL format. To convert a dataset run the following command:

python dataset_converter.py <DATASET DIR> <CONVERTED DATASET DIR> --dataset=<DATASET NAME>

Training

E.g. for training on the TACRED dataset, run the following command:

CUDA_VISIBLE_DEVICES=0 python relation_extraction.py train \
  --write-model True \
  --masking-mode grammar_and_ner \
  --batch-size 8 \
  --max-epochs 3 \
  --lm-coef 0.5 \
  --learning-rate 5.25e-5 \
  --learning-rate-warmup 0.002 \
  --clf-pdrop 0.1 \
  --attn-pdrop 0.1 \
  --word-pdrop 0.0 \
  --dataset tacred \
  --data-dir <CONVERTED DATASET DIR> \
  --seed=0 \
  --log-dir ./logs/

Evaluation

CUDA_VISIBLE_DEVICES=0 python relation_extraction.py evaluate \
  --dataset tacred \
  --masking_mode grammar_and_ner \
  --test_file ./data/tacred/test.jsonl \
  --save_dir ./logs/ \
  --model_file <MODEL FILE (e.g. model_epoch...)> \
  --batch_size 8 \
  --log_dir ./logs/

Trained Models

The models we trained on SemEval and TACRED to produce our paper results can be found here:

Dataset	Masking Mode	P	R	F1	Download
TACRED	grammar_and_ner	70.0	65.0	67.4	Link
SemEval	None	87.6	86.8	87.1	Link

Download and extract model files

First, download the archive corresponding to the model you want to evaluate (links in the table above).

wget --content-disposition <DOWNLOAD URL>

Extract the model archive containing model.pt, text_encoder.pkl, and label_encoder.pkl.

tar -xvzf <MODEL ARCHIVE>

Run evaluation

dataset: dataset to evaluate, can be one of "semeval" or "tacred".
test-file: path to the JSONL test file used during evaluation
log-dir: directory to store the evaluation results and predictions
save-dir: directory containing the downloaded model files (model.pt, text_encoder.pkl, and label_encoder.pkl)
masking-mode: masking mode to use during evaluation, can be one of "None", "grammar_and_ner", "grammar", "ner" or "unk" (caution: must match the mode for training)

For example, to evaluate the TACRED model with "grammar_and_ner" masking, run the following command:

CUDA_VISIBLE_DEVICES=0 python relation_extraction.py evaluate \
      --dataset tacred \
      --test-file ./<CONVERTED DATASET DIR>/test.jsonl \
      --log-dir <RESULTS DIR> \
      --save-dir <MODEL DIR> \
      --masking_mode grammar_and_ner

Citations

If you use our code in your research or find our repository useful, please consider citing our work.

@InProceedings{alt_improving_2019,
  author = {Alt, Christoph and H\"{u}bner, Marc and Hennig, Leonhard},
  title = {Improving Relation Extraction by Pre-trained Language Representations},
  booktitle = {Proceedings of AKBC 2019},
  year = {2019},
  url = {https://openreview.net/forum?id=BJgrxbqp67},
}

License

lm-transformer-re is released under the MIT license. See LICENSE for additional details.

References

Improving language understanding by generative pre-training. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever.
PyTorch implementation of OpenAI's Finetuned Transformer Language Model

DFKI-NLP/TRE