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Coreference Resolution with Entity Equalization

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 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.shand 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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