This repository contains code for the dependency parser from the paper Parsers Know Best: German PP Attachment Revisited published at COLING 2020.
- Python 3.6
- Clone the
bertrepository:git clone https://github.com/google-research/bert.git
- Install dependencies in requirements.txt
Run:
PYTHONPATH=`pwd` python nnparser/model/experiment.py train --helpto see possible arguments.
For example, to train a model on the sample dataset, run:
PYTHONPATH=`pwd` python nnparser/model/experiment.py train \
--train_file data/sample/train.conll \
--dev_file data/sample/dev.conll \
--test_file data/sample/test.conll \
--model_dir runs/sample \
--word_emb_file data/sample/embs.txt \
--word_dim 5 \
--tag_dim 3 \
--hidden_dim 7 \
--num_lstms 2 \
--interval 1 \
--max_epoch 2 \
--shuffle False \
--num_train_buckets 2 \
--num_dev_buckets 2 \
--character_embeddings True \
--char_dim 4 \
--char_hidden_dim 7 \
--working_batch 5000 \
--bert_fine_tuning FalseRun:
PYTHONPATH=`pwd` python nnparser/model/experiment.py eval --helpto see possible arguments.
For example, to evaluate a trained model on the sample dataset, run:
PYTHONPATH=`pwd` python nnparser/model/experiment.py eval \
--test_file data/sample/test.conll \
--output_file runs/sample/results.conll \
--model_dir runs/sampletensorboard --logdir runs/sample/logAll trained models contain:
- File
config.cfgthat records all parameters used to produce the model. - Folder
logrecords training and evaluation metrics, which can be viewed bytensorboard. - See more information at data and models.
@inproceedings{do-rehbein-2020-parsers,
title = "Parsers Know Best: {G}erman {PP} Attachment Revisited",
author = "Do, Bich-Ngoc and Rehbein, Ines",
editor = "Scott, Donia and Bel, Nuria and Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.185",
doi = "10.18653/v1/2020.coling-main.185",
pages = "2049--2061",
}