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Found 5 papers, 3 papers with code
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem.
Normalization of Language Embeddings for Cross-Lingual Alignment
Learning a good transfer function to map the word vectors from two languages into a shared cross-lingual word vector space plays a crucial role in cross-lingual NLP.
On Measuring and Mitigating Biased Inferences of Word Embeddings
Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them.
Learning In Practice: Reasoning About Quantization
There is a mismatch between the standard theoretical analyses of statistical machine learning and how learning is used in practice.
Attenuating Bias in Word Vectors
Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages.
