Uniform Semantic Role Labeling (Under Construction)

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

• Dependency or Span, End-to-End Uniform Semantic Role Labeling
• To appear in AAAI 2019

Part of the codebase is extended from lsgn.

Requirements

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

Getting Started

• sudo apt-get install tcsh (Only required for processing CoNLL05 data)
• GloVe embeddings, the srlconll scripts and the eval09 script:
  ./scripts/fetch_required_data.sh
• Build kernels: ./scripts/build_custom_kernels.sh (Please make adjustments to the script according to your OS/gcc version)

Setting up for ELMo (in progress)

• Some of our models are trained with the ELMo embeddings. We use the ELMo model loaded by tensorflow_hub. You can download the file from here and decompress it into the directory: /elmo.

CoNLL Data

For replicating results on CoNLL-2005, CoNLL-2009 and CoNLL-2012 datasets, please follow the steps below.

CoNLL-2005

The data is provided by: CoNLL-2005 Shared Task, but the original words are from the Penn Treebank dataset, which is not publicly available. If you have the PTB corpus, you can run:
./scripts/fetch_and_make_conll05_data.sh /path/to/ptb/

CoNLL-2009

The data is provided by: CoNLL-2009 Shared Task, Run: ./scripts/make_conll2009_data.sh /path/to/conll-2009

CoNLL-2012

You have to follow the instructions below to get CoNLL-2012 data CoNLL-2012, this would result in a directory called /path/to/conll-formatted-ontonotes-5.0. Run:
./scripts/make_conll2012_data.sh /path/to/conll-formatted-ontonotes-5.0

Training Instructions

• Experiment configurations are found in experiments.conf
• Choose an experiment that you would like to run, e.g. conll2012_best
• For a single-machine experiment, run the following two commands:
  • 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:
  • Run python test_single.py <experiment> for the single-model evaluation. For example: python test_single.py conll2012_final

Other Quirks

• If you want to use GPUs, add command line parameter: -gpu 0.
• 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. The model generally converges at about 300k steps and within 12-36 hours.
• At test time, the code loads the entire GloVe 300D embedding file in the beginning, which would take a while.