# Evaluating cross-lingual textual similarity on dictionary alignment

This repository contains the scripts to prepare the resources as well as open source implementations of the methods. Word Mover's Distance and Sinkhorn implementations are extended from [Cross-lingual retrieval with Wasserstein distance](https://github.com/balikasg/WassersteinRetrieval).

```bash
git clone https://github.com/yigitsever/Evaluating-Dictionary-Alignment.git
cd Evaluating-Dictionary-Alignment
```

## Requirements

```bash
pip install -r pre_requirements.txt
pip install -r requirements.txt
```

- Python 3
- [nltk](http://www.nltk.org/)
- [lapjv](https://pypi.org/project/lapjv/)
- [POT](https://pypi.org/project/POT/)
- [mosestokenizer](https://pypi.org/project/mosestokenizer/)
- (Optional) If using VecMap
    * NumPy
    * SciPy

<details><summary>We recommend using a virtual environment</summary>
<p>

In order to create a [virtual environment](https://docs.python.org/3/library/venv.html#venv-def) that resides in a directory `.env` under home;

```bash
cd ~
mkdir -p .env && cd .env
python -m venv evaluating
source ~/.env/evaluating/bin/activate
```

After the virtual environment is activated, the python interpreter and the installed packages are isolated. In order for our code to work, the correct environment has to be sourced/activated.
In order to install all dependencies automatically use the [pip](https://pypi.org/project/pip/) package installer using `requirements.txt`, which resides under the repository directory.

```bash
# under Evaluating-Dictionary-Alignment
pip install -r requirements.txt
```

Rest of this README assumes that you are in the repository root directory.

</p>
</details>

## Acquiring The Data

nltk is required for this stage;

```python
import nltk
nltk.download('wordnet')
```

Then;

```bash
./get_data.sh
```

This will create two directories; `dictionaries` and `wordnets`.
Linewise aligned definition files are in `wordnets/ready`.

<details><summary>Language pairs and number of available aligned glosses</summary>
<p>
Source Language | Target Language | # of Pairs
--- | ---  | ---:
en | bg | 4959
en | el | 18136
en | it | 12688
en | ro | 58754
en | sl | 3144
en | sq | 4681
bg | el | 2817
bg | it | 2115
bg | ro | 4701
el | it | 4801
el | ro | 2144
el | sq | 4681
it | ro | 10353
ro | sl | 2085
ro | sq | 4646
</p>
</details>

## Acquiring The Embeddings

We use [VecMap](https://github.com/artetxem/vecmap) on [fastText](https://fasttext.cc/) embeddings. You can skip this step if you are providing your own polylingual embeddings.

Otherwise;

* initialize and update the VecMap submodule;

```bash
git submodule init && git submodule update
```

* make sure `./get_data` is already run and `dictionaries` directory is present.

* run;

```bash
./get_embeddings.sh
```

Bear in mind that this will require around 50 GB free space.

## Quick Demo

`demo.sh` is included, downloads data for 2 languages.

```bash
./demo.sh
```