Euclidean vs. Graph-Based Framings for Bilingual Lexicon Induction

This is an implementation of the experiments and combination system presented in:

• Kelly Marchisio, Youngser Park, Ali Saad-Eldin,, Anton Alyakin Kevin Duh, Carey Priebe, and Philipp Koehn. 2021. An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces. In Findings of the Association for Computational Linguistics: EMNLP 2021.

If you use this software for academic research, please cite the paper above.

Requirements

• Python3
• CuPy
• Dask
• sklearn
• scipy

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Setup

To download pretrained word embeddings, run sh get_data.sh from the embs/ folder. To download MUSE dictionaries and create development sets, run sh create_dicts.sh from the dicts/ folder. To download Graspologic and Vecmap, run sh get_packages.sh from the third_party/ folder.

Usage

Note: All results are written to the exp/ directory.

To run the non-iterative experiments presented in Table 2 of the publication, run, for example:

sh exps.sh single en de proc 100

for a single run of English-German using the "Procrustes" method (Euclidean view) and 100 seeds, or the below for SGM (Graph-based view):

sh exps.sh single en de sgm 100

To run iterative experiments presented in Table 4, one may run:

sh exps.sh add-all en de proc 100
sh exps.sh stoch-add en de proc 100
sh exps.sh active-learn en de proc 100

For the combination system presented in Section 6, one may run the below for English-German, starting with Iterative Procrustes and 100 seeds (Start: IterProc from Table 5):

sh combo-exps.sh en de proc 100

Output will be streamed to stdout. P@1 for -PullSGM for this example will be seen at the end of program running. For -PullProc, one reads the P@1 for the Forward direction of the last run of Iterative Procrustes.