This repository contains the code implementation from the paper "Coreference Resolution without Span Representations".
Set up a virtual environment and run:
pip install -r requirements.txt
Follow the Quick Start to enable mixed precision using apex.
Run (from inside the repo):
git clone https://github.com/conll/reference-coreference-scorers.git
This repo assumes access to the OntoNotes 5.0 corpus. Convert the original dataset into jsonlines format using:
export DATA_DIR=<data_dir>
python minimize.py $DATA_DIR
Credit: This script was taken from the e2e-coref repo.
Download our trained model:
export MODEL_DIR=<model_dir>
curl -L https://www.dropbox.com/sh/7hpw662xylbmi5o/AAC3nfP4xdGAkf0UkFGzAbrja?dl=1 > temp_model.zip
unzip temp_model.zip -d $MODEL_DIR
rm -rf temp_model.zip
and run:
export OUTPUT_DIR=<output_dir>
export CACHE_DIR=<cache_dir>
export MODEL_DIR=<model_dir>
export DATA_DIR=<data_dir>
export SPLIT_FOR_EVAL=<dev or test>
python run_coref.py \
--output_dir=$OUTPUT_DIR \
--cache_dir=$CACHE_DIR \
--model_type=longformer \
--model_name_or_path=$MODEL_DIR \
--tokenizer_name=allenai/longformer-large-4096 \
--config_name=allenai/longformer-large-4096 \
--train_file=$DATA_DIR/train.english.jsonlines \
--predict_file=$DATA_DIR/test.english.jsonlines \
--do_eval \
--num_train_epochs=129 \
--logging_steps=500 \
--save_steps=3000 \
--eval_steps=1000 \
--max_seq_length=4096 \
--train_file_cache=$DATA_DIR/train.english.4096.pkl \
--predict_file_cache=$DATA_DIR/test.english.4096.pkl \
--amp \
--normalise_loss \
--max_total_seq_len=5000 \
--experiment_name=eval_model \
--warmup_steps=5600 \
--adam_epsilon=1e-6 \
--head_learning_rate=3e-4 \
--learning_rate=1e-5 \
--adam_beta2=0.98 \
--weight_decay=0.01 \
--dropout_prob=0.3 \
--save_if_best \
--top_lambda=0.4 \
--tensorboard_dir=$OUTPUT_DIR/tb \
--conll_path_for_eval=$DATA_DIR/$SPLIT_FOR_EVAL.english.v4_gold_conll
Train a coreference model using:
export OUTPUT_DIR=<output_dir>
export CACHE_DIR=<cache_dir>
export DATA_DIR=<data_dir>
python run_coref.py \
--output_dir=$OUTPUT_DIR \
--cache_dir=$CACHE_DIR \
--model_type=longformer \
--model_name_or_path=allenai/longformer-large-4096 \
--tokenizer_name=allenai/longformer-large-4096 \
--config_name=allenai/longformer-large-4096 \
--train_file=$DATA_DIR/train.english.jsonlines \
--predict_file=$DATA_DIR/dev.english.jsonlines \
--do_train \
--do_eval \
--num_train_epochs=129 \
--logging_steps=500 \
--save_steps=3000 \
--eval_steps=1000 \
--max_seq_length=4096 \
--train_file_cache=$DATA_DIR/train.english.4096.pkl \
--predict_file_cache=$DATA_DIR/dev.english.4096.pkl \
--gradient_accumulation_steps=1 \
--amp \
--normalise_loss \
--max_total_seq_len=5000 \
--experiment_name="s2e-model" \
--warmup_steps=5600 \
--adam_epsilon=1e-6 \
--head_learning_rate=3e-4 \
--learning_rate=1e-5 \
--adam_beta2=0.98 \
--weight_decay=0.01 \
--dropout_prob=0.3 \
--save_if_best \
--top_lambda=0.4 \
--tensorboard_dir=$OUTPUT_DIR/tb \
--conll_path_for_eval=$DATA_DIR/dev.english.v4_gold_conll
To evaluate your trained model on test go here.
If you use this code in your research, please cite our paper:
@article{kirstain2021coreference,
title={Coreference Resolution without Span Representations},
author={Kirstain, Yuval and Ram, Ori and Levy, Omer},
journal={arXiv preprint arXiv:2101.00434},
year={2021}
}
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