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Coreference Resolution With Entity Equalization

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# Coreference Resolution with Entity Equalization

## Introduction
This repository contains the code for replicating results from

* [Coreference Resolution with Entity Equalization](https://www.aclweb.org/anthology/P19-1066)
* In ACL 2019
* The baseline model is from the paper [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://arxiv.org/abs/1804.05392)
* Code for baseline model: [https://github.com/kentonl/e2e-coref](https://github.com/kentonl/e2e-coref)

## Getting Started

* Install python (either 2 or 3) requirements: `pip install -r requirements.txt`
* 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`and `extract_bert_features.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.

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