Character Identification on Dialogue with Neural Coreference Resolution

Introduction

This repository is to accomplish a shared task in SemEval 2018 - Task 4: Character Identification on Multiparty Dialogues. Main references are listed in the following:

• SemEval 2018 - Task 4: Character Identification on Multiparty Dialogues
• End-to-end Neural Coreference Resolution
  • A demo of the code can be found here: http://www.kentonl.com/e2e-coref.
• Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts

Requirements

• Python 2.7
  • TensorFlow 1.4.0
  • pyhocon (for parsing the configurations)
  • NLTK (for sentence splitting and tokenization in the demo)

pip install -r requirements.txt

Setting Up

• Download pretrained word 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.
• Run one of the following:
  • To use the pretrained model only, run setup_pretrained.sh
  • To train your own models, run setup_training.sh
    • This assumes access to OntoNotes 5.0. Please edit the ontonotes_path variable.

Training Instructions

Coreference Resolution

• Experiment configurations are found in experiments.conf
• Choose an experiment that you would like to run, e.g. best
• For a single-machine experiment, run the following two commands:
  • python singleton.py <experiment>
  • python evaluator.py <experiment>
• For a distributed multi-gpu experiment, edit the cluster property of the configuration and run the following commands:
  • python parameter_server.py <experiment>
  • python worker.py <experiment> (for every worker in your cluster)
  • python evaluator.py <experiment> (on the same machine as your first worker)
• Results are stored in the logs directory and can be viewed via TensorBoard.
• For final evaluation of the checkpoint with the maximum dev F1:
  • Run python test_single.py <experiment> for the single-model evaluation.
  • Run python test_ensemble.py <experiment1> <experiment2> <experiment3>... for the ensemble-model evaluation.

Entity Linking

• Prepare mention embedding data:
  • python entity_linking_helper.py <experiment>
• Train entity linking model:
  • python entity_linking_train.py <experiment>
• Test entity linkning model:
  • python entity_linking_test.py <experiment>

Demo Instructions

• For the command-line demo with the pretrained model:
  • Run python demo.py final
• For the web demo with the pretrained model:
  • Run python demo.py final 8080
  • Edit the URL at the end of docs/main.js to point to the demo location, e.g. localhost:8080
  • Open docs/index.html in a web browser.
• To run the demo with other experiments, replace final with your configuration name.

Contact

• Aoxuan Li a811278305@gmail.com
• Pu-Chin Chen puchinchen@ucla.edu
• Xin Liu xinliu627@ucla.edu
• Yutian Zhang yutianzh0527@gmail.com