https://github.com/swabhs/scaffolding
Frame-Semantic and PropBank Semantic Role Labeling with Syntactic Scaffolding.
https://github.com/swabhs/scaffolding
Last synced: 5 months ago
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Frame-Semantic and PropBank Semantic Role Labeling with Syntactic Scaffolding.
- Host: GitHub
- URL: https://github.com/swabhs/scaffolding
- Owner: swabhs
- Created: 2018-08-28T04:42:33.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-06-27T23:12:01.000Z (almost 4 years ago)
- Last Synced: 2024-12-16T12:15:43.193Z (5 months ago)
- Language: Python
- Homepage:
- Size: 2.03 MB
- Stars: 50
- Watchers: 3
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
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README
# Scaffolding: Syntactic Scaffolds for Semantic Structures
A repository based on the EMNLP 2018 [paper](https://arxiv.org/abs/1808.10485) for Frame-Semantic and PropBank Semantic Role Labeling with Syntactic Scaffolding.
## Installation
This repository was built on an earlier version of [AllenNLP](https://github.com/allenai/allennlp).
Due to changes in the API, we recommended installing directly via steps below (adapted from the AllenNLP installation), as opposed to using an installed version of AllenNLP.
1. [Download and install Conda](https://conda.io/docs/download.html).
2. Create a Conda environment with Python 3.6
```
conda create -n scaffold python=3.6
```
3. Activate the Conda environment. (You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.
```
source activate scaffold
```
4. Install in your environment
```
git clone https://github.com/swabhs/scaffolding.git
```
5. Change your directory to where you cloned the files:
```
cd scaffolding/
```
6. Install the required dependencies.
```
INSTALL_TEST_REQUIREMENTS="true" ./scripts/install_requirements.sh
```
7. Install PyTorch version 0.3 **via pip** ([modify](https://pytorch.org/previous-versions/) based on your CUDA environment).
Conda-based installation results in [slower rutime](https://github.com/pytorch/pytorch/issues/537) because of a CUDA issue.
```
pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl
```
## Step 1: Get Data
Download [FN data](https://drive.google.com/file/d/15n3M4AmURGdGqnNAjn352buUTV5S-fVI/view?usp=sharing) and place it under a `data/` directory under the root directory.
The [OntoNotes datasets](https://catalog.ldc.upenn.edu/LDC2013T19) can be obtained through LDC.
## Step 2: Get Pre-trained Models
[Frame-SRL baseline](https://drive.google.com/open?id=1f7ZLOBc65Y74hPQlYY8mGVHkCiN14dwH)
[Frame-SRL scaffold with common non-terminals](https://drive.google.com/open?id=1V1-U70U-wDKaG3zuONQN3eB77jjS3FSd)
[PropBank SRL baseline](https://drive.google.com/file/d/1ThTagaJeZkIACEYfDn6f4mjMHs0PMSAo/view?usp=sharing)
[PropBank SRL scaffold with common non-terminals](https://drive.google.com/file/d/1vv3KC_OLx0A7ItKqBz9yWwZLIsVZhR5B/view?usp=sharing)
## Step 3: Test
```
python -m allennlp.run evaluate \
--archive-file log_final_pb_baseline/model.tar.gz \
--evaluation-data-file data/fndata-1.5/test/fulltext/ \
--cuda-device 0
```
For the syntactic scaffold model for PropBank SRL, use the `pbscaf` branch:
```
git checkout pbscaf
```
and then run evaluation as above.
## Training
For scaffolds, use `$command=train_m` and for baselines, `$command=train`.
```
python -m allennlp.run $command training_config/$config --serialization-dir log
```
## Acknowledgment
This is based on our [paper](https://arxiv.org/abs/1808.10485), please cite:
```
@inproceedings{swayamdipta:2018,
author = {Swabha Swayamdipta and Sam Thomson and Kenton Lee and Luke Zettlemoyer and Chris Dyer and Noah A. Smith},
title = {Syntactic Scaffolding for Semantic Structures},
booktitle = {Proc. of EMNLP},
url = {https://arxiv.org/abs/1808.10485},
year = {2018}
}
```