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https://github.com/lgirrbach/sigmorphon-2023-inflection
Code for our SIGMORPHON 2023 shared task description paper: Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention
https://github.com/lgirrbach/sigmorphon-2023-inflection
inflection morphological-inflection morphology seq2seq straight-through-gradient transducer
Last synced: 2 days ago
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Code for our SIGMORPHON 2023 shared task description paper: Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention
- Host: GitHub
- URL: https://github.com/lgirrbach/sigmorphon-2023-inflection
- Owner: LGirrbach
- Created: 2023-03-06T08:30:50.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-06T10:04:19.000Z (over 1 year ago)
- Last Synced: 2023-08-06T12:22:09.203Z (over 1 year ago)
- Topics: inflection, morphological-inflection, morphology, seq2seq, straight-through-gradient, transducer
- Language: Python
- Homepage: https://aclanthology.org/2023.sigmorphon-1.17/
- Size: 102 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention
## Introduction
This repository contains code for our system description paper [Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention](https://aclanthology.org/2023.sigmorphon-1.17/) for the [Morphological Inflection shared task](https://aclanthology.org/2023.sigmorphon-1.13/).
Note, that the code for the interlinear glossing model can be found [in this repository](https://github.com/LGirrbach/sigmorphon-2023-glossing).
The implementation of the hard-attention transducer is in [model.py](model.py).
## Setup
Create a virtual environment, e.g. by using [Anaconda](https://docs.conda.io/en/latest/miniconda.html):
```
conda create -n inflection python=3.9 pip
```
Activate the environment:
```
conda activate inflection
```
Then, install the dependencies in [requirements.txt](requirements.txt):
```
pip install -r requirements.txt
```
Finally, place the shared task data in the repository, i.e. there should be a folder called `data`. The data can be obtained from [the shared task's main repository](https://github.com/sigmorphon/2023InflectionST/tree/main/part1).
## Train a model
To train a single model and get predictions for the corresponding test set, run
```
python experiment.py --language LANGUAGE
```
Here, `LANGUAGE` is a (three letter) code for the language. We assume the respective files `LANGUAGE.trn`, `LANGUAGE.dev` and `LANGUAGE.covered.tst` are in `./data`.
To see all command line options and hyperparameters, run
```
python experiment.py --help
```
## Hyperparameter tuning
To tune hyperparameters, use the script [hyperparameter_tuning.py](hyperparameter_tuning.py).
The main parameters are `--language` which specifies the dataset as before, and `--trials`, which specifies the number of evaluated hyperparameter combinations.
To parse the results from tuning logs, we provide the scrip [parse_hyperparameters.py](parse_hyperparameters.py).
The best parameters from our tuning runs are in [best_hyperparameters.json](best_hyperparameters.json).
We evaluated 50 trials per language to get these parameters.
Finally, we provide a script [train_best_parameters.py](train_best_parameters.py), that retrains models (from scratch) using the hyperparameters in [best_hyperparameters.json](best_hyperparameters.json).
## Citation
If you use this code, consider citing our paper:
```
@inproceedings{girrbach-2023-tu,
title = {T{\"u}-{CL} at {SIGMORPHON} 2023: Straight-Through Gradient Estimation for Hard Attention},
author = "Girrbach, Leander",
booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigmorphon-1.17",
pages = "151--165",
}
```