Awesome

Labeled Span Graph Network (Under Construction)

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

• Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
• Luheng He, Kenton Lee, Omer Levy and Luke Zettlemoyer
• In ACL 2018

Part of the codebase is extended from e2e-coref.

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 and the srlconll scripts:
  ./scripts/fetch_required_data.sh
• Build kernels: ./scripts/build_custom_kernels.sh (Please make adjustments to the script according to your OS/gcc version)
• Download pretrained models by running ./scripts/fetch_all_models.sh

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.
• It is recommended to cache ELMo embeddings for training and validating efficiency. Instructions will be added soon.

Making Predictions with Pretrained Models

• Please see data/sample.jsonlines for input format (json). Each json object can contain multiple sentences.
• For example, run python decoder.py conll2012_final data/sample.jsonlines sample.out to predict SRL structures.
• The output will also be in json format, with an additional array storing the SRL tuples. For example, for the following input sentences:

[["John", "told", "Pat", "to", "stop", "the", "robot", "immediately", "."], ["Pat", "refused", "."]]

The following json object

"predicted_srl": [[1, 0, 0, "ARG0"], [1, 2, 2, "ARG2"], [1, 3, 7, "ARG1"], [4, 2, 2, "ARG0"], [4, 5, 6, "ARG1"], [4, 7, 7, "ARGM-TMP"], [10, 9, 9, "ARG0"]]

contains SRL predictions for the two sentences, formatted as [predicate_position, argument_span_start, argument_end, role_label]. The token ids are counted starting 0 from the beginning of the document (instead of the beginning of each sentence).

CoNLL Data

For replicating results on CoNLL-2005 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-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

• 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 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 48 hours.
• At test time, the code loads the entire GloVe 300D embedding file in the beginning, which would take a while.