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# 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) and supervised implementation is extended from https://github.com/fionn-mac/Manhattan-LSTM.
```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/)
- 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 your home directory;
```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 within. 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. `pre_requirements.text` includes requirements that packages in `requirements.txt` depend on. Both files come with the repository, so first navigate to the repository and then;
```bash
# under Evaluating-Dictionary-Alignment
pip install -r pre_requirements.txt
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`. Definition files that are used by the unsupervised methods are in `wordnets/ready`, they come in pairs, `a_to_b.def` and `b_to_a.def` for wordnet definitions in language `a` and `b`. The pairs are aligned linewise; definitions on the same line for either file belong to the same wordnet synset, in the respective language.
<details><summary>Language pairs and number of available aligned glosses</summary>
<p>
Source Language | Target Language | # of Pairs
--- | --- | ---:
English | Bulgarian | 4959
English | Greek | 18136
English | Italian | 12688
English | Romanian | 58754
English | Slovenian | 3144
English | Albanian | 4681
Bulgarian | Greek | 2817
Bulgarian | Italian | 2115
Bulgarian | Romanian | 4701
Greek | Italian | 4801
Greek | Romanian | 2144
Greek | Albanian | 4681
Italian | Romanian | 10353
Romanian | Slovenian | 2085
Romanian | Albanian | 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. The mapped embeddings are stored under `bilingual_embedings` using the same naming scheme that `.def` files use.
## Quick Demo
`demo.sh` is included, downloads data for 2 languages and runs WMD (Word Mover's Distance) and SNK (Sinkhorn Distance) methods in matching and retrieval paradigms.
```bash
./demo.sh
```
## Usage
### WMD.py - Word Mover's Distance and Sinkhorn Distance
Aligns definitions using WMD or SNK metrics and matching or retrieval paradigms.
```
usage: WMD.py [-h] [-b] [-n INSTANCES]
source_lang target_lang source_vector target_vector source_defs
target_defs {all,wmd,snk} {all,retrieval,matching}
align dictionaries using wmd and wasserstein distance
positional arguments:
source_lang source language short name
target_lang target language short name
source_vector path of the source vector
target_vector path of the target vector
source_defs path of the source definitions
target_defs path of the target definitions
{all,wmd,snk} which methods to run
{all,retrieval,matching}
which paradigms to align with
optional arguments:
-h, --help show this help message and exit
-b, --batch running in batch (store results in csv) or running a
single instance (output the results)
-n INSTANCES, --instances INSTANCES
number of instances in each language to retrieve
```
Example;
```
python WMD.py en bg bilingual_embeddings/en_to_bg.vec bilingual_embeddings/bg_to_en.vec wordnets/ready/en_to_bg.def wordnets/ready/bg_to_en.def wmd retrieval
```
Will run on English and Bulgarian definitions, using WMD for retrieval.
### sentence_embedding.py - Sentence Embedding Representation
```
usage: sentence_embedding.py [-h] [-n INSTANCES] [-b]
source_lang target_lang source_vector
target_vector source_defs target_defs
{all,retrieval,matching}
align dictionaries using sentence embedding representation
positional arguments:
source_lang source language short name
target_lang target language short name
source_vector path of the source vector
target_vector path of the target vector
source_defs path of the source definitions
target_defs path of the target definitions
{all,retrieval,matching}
which paradigms to align with
optional arguments:
-h, --help show this help message and exit
-n INSTANCES, --instances INSTANCES
number of instances in each language to use
-b, --batch running in batch (store results in csv) or running a
single instance (output the results)
```
Example;
```
python sentence_embedding.py it ro bilingual_embeddings/it_to_ro.vec bilingual_embeddings/ro_to_it.vec wordnets/ready/it_to_ro.def wordnets/ready/ro_to_it.def matching
```
Will run on Italian and Romanian definitions, using sentence embedding representation for matching.
### learn_and_predict.py - Supervised Alignment
```
usage: learn_and_predict.py [-h] -sl SOURCE_LANG -tl TARGET_LANG -df DATA_FILE
-es SOURCE_EMB_FILE -et TARGET_EMB_FILE
[-l MAX_LEN] [-z HIDDEN_SIZE] [-b] [-n NUM_ITERS]
[-lr LEARNING_RATE]
optional arguments:
-h, --help show this help message and exit
-sl SOURCE_LANG, --source_lang SOURCE_LANG
Source language.
-tl TARGET_LANG, --target_lang TARGET_LANG
Target language.
-df DATA_FILE, --data_file DATA_FILE
Path to dataset.
-es SOURCE_EMB_FILE, --source_emb_file SOURCE_EMB_FILE
Path to source embedding file.
-et TARGET_EMB_FILE, --target_emb_file TARGET_EMB_FILE
Path to target embedding file.
-l MAX_LEN, --max_len MAX_LEN
Maximum number of words in a sentence.
-z HIDDEN_SIZE, --hidden_size HIDDEN_SIZE
Number of units in LSTM layer.
-b, --batch running in batch (store results to csv) or running in
a single instance (output the results)
-n NUM_ITERS, --num_iters NUM_ITERS
Number of iterations/epochs.
-lr LEARNING_RATE, --learning_rate LEARNING_RATE
Learning rate for optimizer.
```
Example;
```
python learn_and_predict.py -sl en -tl ro -df ./wordnets/tsv_files/en_to_ro.tsv -es bilingual_embeddings/en_to_ro.vec -et bilingual_embeddings/ro_to_en.vec
```
Will run on English and Romanian definitions.
|
