Skip to content

Latest commit

 

History

History
80 lines (64 loc) · 5.15 KB

README.md

File metadata and controls

80 lines (64 loc) · 5.15 KB
Raw

End-to-End Coreference Resolution with Different Higher-Order Inference Methods

This repository contains the implementation of the paper: Revealing the Myth of Higher-Order Inference in Coreference Resolution.

Architecture

The basic end-to-end coreference model is a PyTorch re-implementation based on the TensorFlow model following similar preprocessing (see this repository).

There are four higher-order inference (HOI) methods experimented: Attended Antecedent, Entity Equalization, Span Clustering, and Cluster Merging. All are included here except for Entity Equalization which is experimented in the equivalent TensorFlow environment (see this separate repository).

Files:

Basic Setup

Set up environment and data for training and evaluation:

  • Install Python3 dependencies: pip install -r requirements.txt
  • Create a directory for data that will contain all data files, models and log files; set data_dir = /path/to/data/dir in experiments.conf
  • Prepare dataset (requiring OntoNotes 5.0 corpus): ./setup_data.sh /path/to/ontonotes /path/to/data/dir

For SpanBERT, download the pretrained weights from this repository, and rename it /path/to/data/dir/spanbert_base or /path/to/data/dir/spanbert_large accordingly.

Evaluation

Provided trained models:

  • SpanBERT + no HOI: FILE
  • SpanBERT + Attended Antecedent: FILE
  • SpanBERT + Span Clustering: FILE
  • SpanBERT + Cluster Merging: FILE
  • SpanBERT + Entity Equalization: see repository

The name of each directory corresponds with a configuration in experiments.conf. Each directory has two trained models inside.

If you want to use the official evaluator, download and unzip conll 2012 scorer under this directory.

Evaluate a model on the dev/test set:

  • Download the corresponding model directory and unzip it under data_dir
  • python evaluate.py [config] [model_id] [gpu_id]
    • e.g. Attended Antecedent:python evaluate.py train_spanbert_large_ml0_d2 May08_12-38-29_58000 0

Prediction

Prediction on custom input: see python predict.py -h

  • Interactive user input: python predict.py --config_name=[config] --model_identifier=[model_id] --gpu_id=[gpu_id]
    • E.g. python predict.py --config_name=train_spanbert_large_ml0_d1 --model_identifier=May10_03-28-49_54000 --gpu_id=0
  • Input from file (jsonlines file of this format): python predict.py --config_name=[config] --model_identifier=[model_id] --gpu_id=[gpu_id] --jsonlines_path=[input_path] --output_path=[output_path]

Training

python run.py [config] [gpu_id]

  • [config] can be any configuration in experiments.conf
  • Log file will be saved at your_data_dir/[config]/log_XXX.txt
  • Models will be saved at your_data_dir/[config]/model_XXX.bin
  • Tensorboard is available at your_data_dir/tensorboard

Configurations

Some important configurations in experiments.conf:

  • data_dir: the full path to the directory containing dataset, models, log files
  • coref_depth and higher_order: controlling the higher-order inference module
  • bert_pretrained_name_or_path: the name/path of the pretrained BERT model (HuggingFace BERT models)
  • max_training_sentences: the maximum segments to use when document is too long; for BERT-Large and SpanBERT-Large, set to 3 for 32GB GPU or 2 for 24GB GPU

Citation

@inproceedings{xu-choi-2020-revealing,
    title = "Revealing the Myth of Higher-Order Inference in Coreference Resolution",
    author = "Xu, Liyan  and  Choi, Jinho D.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.686",
    pages = "8527--8533"
}