Several studies have explored various advantages of multilingual pre-trained models (such
as multilingual BERT) in capturing shared linguistic knowledge. However, less attention has …

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. It is useful in …

In recent years, word embeddings (WEs) have proven relevant for studying differences and
similarities among job professions and skills required by the labour market across countries …

The current state-of-the-art for few-shot cross-lingual transfer learning first trains on
abundant labeled data in the source language and then fine-tunes with a few examples on …

We propose novel structural-based approaches for the generation and comparison of cross
lingual sentence representations. We do so by applying geometric and topological methods …

Language models have been shown to reproduce underlying biases existing in their training
data, which is the majority perspective by default. Proposed solutions aim to capture minority …

Word embeddings have proven extremely useful across many NLP applications in recent
years. Several key linguistic tasks, such as machine translation and transfer learning …

In contemporary natural language processing (NLP) tasks, it is common to utilize the
representation of large language models (LLMs) directly in downstream applications …

Transformer based Large language models such as BERT, have demonstrated the ability to
derive contextual information from the words surrounding it. However, when these models …

The aim of the Bilingual Lexicon Induction (BLI) task is to produce a bilingual lexicon using a
pair of comparable corpora and either a small set of seed translations (a supervised setting) …