Higher-order Coreference Resolution with Coarse-to-fine Inference
Introduction
This repository contains the code for replicating results from
Getting Started
- Install python (either 2 or 3) requirements:
pip install -r requirements.txt
- Download pretrained models at https://drive.google.com/file/d/1fkifqZzdzsOEo0DXMzCFjiNXqsKG_cHi
- Move the downloaded file to the root of the repo and extract:
tar -xzvf e2e-coref.tgz
- Download GloVe embeddings and build custom kernels by running
setup_all.sh.
- There are 3 platform-dependent ways to build custom TensorFlow kernels. Please comment/uncomment the appropriate lines in the script.
- To train your own models, run
setup_training.sh
- This assumes access to OntoNotes 5.0. Please edit the
ontonotes_path variable.
Training Instructions
- Experiment configurations are found in
experiments.conf
- Choose an experiment that you would like to run, e.g.
best
- Training:
python train.py <experiment>
- Results are stored in the
logs directory and can be viewed via TensorBoard.
- Evaluation:
python evaluate.py <experiment>
Demo Instructions
- Command-line demo:
python demo.py final
- To run the demo with other experiments, replace
final with your configuration name.
Batched Prediction Instructions
- Create a file where each line is in the following json format (make sure to strip the newlines so each line is well-formed json):
{
"clusters": [],
"doc_key": "nw",
"sentences": [["This", "is", "the", "first", "sentence", "."], ["This", "is", "the", "second", "."]],
"speakers": [["spk1", "spk1", "spk1", "spk1", "spk1", "spk1"], ["spk2", "spk2", "spk2", "spk2", "spk2"]]
}
clusters should be left empty and is only used for evaluation purposes.doc_key indicates the genre, which can be one of the following: "bc", "bn", "mz", "nw", "pt", "tc", "wb"speakers indicates the speaker of each word. These can be all empty strings if there is only one known speaker.
- Run
python predict.py <experiment> <input_file> <output_file>, which outputs the input jsonlines with predicted clusters.
Other Quirks
- It does not use GPUs by default. Instead, it looks for the
GPU environment variable, which the code treats as shorthand for CUDA_VISIBLE_DEVICES.
- The training runs indefinitely and needs to be terminated manually. The model generally converges at about 400k steps.