/DyGIE

Primary LanguagePython

DyGIE (Under Construction)

This repository contains code and models for replicating results from the following publication:

Part of the codebase is extended from SciIE, lsgn and e2e-coref.

Requirements

  • Python 2.7
    • TensorFlow 1.8.0
    • pyhocon (for parsing the configurations)
    • tensorflow_hub (for loading ELMo)

Getting Started

  • Python 2.7

  • TensorFlow 1.8.0

  • pyhocon (for parsing the configurations)

  • tensorflow_hub (for ELMo)

  • GloVe embeddings and downloading data:
    ./scripts/fetch_required_data.sh which will download the GLoVe Embeddings, pre-processed dataset (in jason format) and cached ELMo embeddings. The link to SciERC, WLP and GENIA datasets are included. Scripts for processing ACE05, ACE04 and ontonotes to jason format are included in preprocessing folder

  • Build kernels: ./model1/build_custom_kernels.sh (Please make adjustments to the script based on your OS/gcc version)

Setting up for ELMo (if you use your own data)

  • Some of our models are trained with the ELMo embeddings. We use the ELMo model loaded by tensorflow_hub.
  • It is recommended to cache ELMo embeddings for training and validating efficiency. Please modify the corresponding filenames and run python generate_elmo.py to generate ELMo embeddings for your own data.

Two Variations of DyGIE

  • model1: Coreference propagation layer at the bottom and relation propagation layer at the top (same as described in Figure 2 in the paper). This model architecture is used for all datasets all tasks except for ACE05 NER task.
  • model2: Relation propagation layer at the bottom and coreference propagation layer at the top (swap CorefProp and RelProp in Figure 2). This model architecture is used for ACE05 NER task.

Training Instructions

  • Experiment configurations are found in model*/experiments.conf

  • The parameter main_metrics can be selected from coref, ner, relation or any combination of the three, such as coref_ner_relation which indicates the F1 score for averaged F1 score for coref, ner and relation. The model is tuned and saved based on the resulting averaged F1 score.

  • The parameters ner_weight and relation_weight are weights for the multi-task objective. If set the weight to 0 then the task is not trained.

  • If coreference is used as an auxiliary task, coref_weight is always set to 0. The training of the main task and the training of coreference objective functions take turns. The frequency of how often the auxiliary task is trained is controlled by the parameter coref_freq.

  • If training coreference as the main task, set coref_weight to 1 and coref_only flag to 1.

  • The number of iteration in CorefProp is controlled by coref_depth, the number of iteration in RelProp is controlled by rel_prop

  • Choose an experiment that you would like to run, e.g. genia_best_ner

  • For a single-machine experiment, run the following two commands in parallel:

    • python singleton.py <experiment>
    • python evaluator.py <experiment>

  • Results are stored in the logs directory and can be viewed via TensorBoard.

  • For final evaluation of the checkpoint with the maximum dev F1:

    • python test_single.py <experiment>

Other Quirks

  • Use the parameter gpu to set the gpu ID. If using CPU, set gpu to any arbitary number.
  • The evaluator should not be run on GPUs, since evaluating full documents does not fit within GPU memory constraints.
  • The training runs indefinitely and needs to be terminated manually.

Making Predictions with Pretrained Models

  • Define the output path in experiments.conf as output_path, the system will output the results of eval_path to output_path. The output file is also a json file, which has thesame format as eval_path. Then run python write_single.py <experiment>

Best Models