dsfsi/cross-lingual-transfer-gains-evaluation

Code and datasets for studying cross-lingual embedding transfer and transfer gain analysis in mutually intelligible African languages using VecMap and MUSE. # Overview This repository accompanies our study on cross-lingual word embeddings for mutually intelligible African languages. We investigate how cross-lingual projection methods, including VecMap and MUSE, affect embedding quality and introduce a transfer gain framework for quantifying positive and negative transfer relative to monolingual baselines. The repository contains code for generating monolingual and cross-lingual embeddings, conducting bilingual lexicon ablations, and evaluating semantic similarity using the WordSim-353 and SimLex-999 benchmarks. ## `fasttext_ablation.py` The `fasttext_ablation.py` script is used to train monolingual embeddings using FastText across different training data sizes. The data is stored in 'temp_corpora'. It performs dataset ablation experiments by varying the amount of input text used to train each embedding model, allowing us to study how embedding quality changes with data scale. The script generates multiple FastText models per language, each trained on a different subset of the dataset, which are later used for evaluation on semantic similarity benchmarks such as WordSim-353 and SimLex-999. ## `Monolingual_Evaluation.py` The `Monolingual_Evaluation.py` script is used to evaluate the quality of the trained monolingual FastText embeddings. It computes embedding performance using Spearman’s rank correlation coefficient to measure alignment with human-annotated semantic similarity and relatedness judgments. The evaluation is performed on standard benchmarks, including SimLex-999 and WordSim-353, which are stored in the `BenchMarks/` directory. This script provides a consistent framework for assessing how well the learned embeddings capture semantic structure in a monolingual setting, and serves as the baseline for subsequent cross-lingual transfer experiments. This is ran through `fasttext_eval_script.sh` ## `VecMap/` and `MUSE/` The `VecMap/` and `MUSE/` directories contain implementations for generating cross-lingu …