Subword-alignment based cross-lingual word embeddings

This is the official implementation of our paper Subword-alignment based cross-lingual word embeddings in CICLing 2019, which learns cross-lingual word embeddings by exploiting subword-alignment

Setup

Clone this repository

$ git clone https://github.com/jyori112/sabclwe

Then install required python packages

$ pip install -r requirements.txt

Other Requirements

1. mpaligner: Please install mpaligner into mpaligner_0.97 (alignment tool to obtain subword-alignment model) from: https://osdn.net/projects/mpaligner/.

2. VecMap: Please install vecmap into vecmap (tool to obtain cross-lingual word embeddings) from: https://github.com/artetxem/vecmap.

Usage

For the exact usage of mpaligner and vecmap, please read the official documentations.

Dictionary induction

Given cross-lingual (bilingual) word embeddings [LANG1_EMB] and [LANG2_EMB], first, induce bilingual dictionary by

$ python -m induce_dict [LANG1_EMB] [LANG2_EMB] --csls 10 > [DICT_PATH]

The resulting file ([DICT_PATH]) contains two words followed by the similarity.

Align dictionary

To train an align model, we first preprocess the dictionary file

$ cat [DICT_PATH]| cut -f1,2| perl mpaligner_0.97/script/separate_for_char.pl > [PREPROCESSED_DICT_PATH]

Then, we train the alignment model

$ mpaligner_0.97/mpaligner -i [PREPROCESSED_DICT_PATH] -s >

This will produce alignment file in [PREPROCESSED_DICT_PATH].align

Finally, we reformat the resulting file by

$ python -m parse_aligned < [PREPROCESSED_DICT_PATH].align| sort -rnk3 > [ALIGNMENT_DICT_PATH]

Filter dictionary

To filter dictionary by [THRESHOLD]

$ awk '{ if ($3 > [THRESHOLD]) print $1,$2}' < [ALIGNMENT_DICT_PATH] > [FILTERED_DICT]

Train cross-lingual word embeddings from the filtered dictionary

Use vecmap tool to obtain cross-lingual word embeddings

$ python vecmap/map_embeddings.py [LANG1_EMB] [LANG2_EMB] [LANG1_EMB_OUTPUT] [LANG2_EMB_OUTPUT] --supervised

Citation

To Appear.