To bridge the gaps between languages, numerous CLT algorithms have emerged, ranging from early translation-based methods (Prettenhofer and Stein, 2010), cross-lingual word representation learning (Conneau et al., 2018a), to powerful mPLM (Devlin et al., 2019;Lample and Conneau, 2019), from which the versatile multilingual representations derived suffice it to become a mainstream approach for various downstream CLT tasks.
another research efforts made on cross-lingual word representation learning (Zou et al., 2013;Mikolov et al., 2013;Conneau et al., 2018a;artetxe et al., 2018) and mPLM (Devlin et al., 2019;Lample and Conneau, 2019;Eisenschlos et al., 2019;Chidambaram et al., 2019), which exploit unsupervised learning on large-scale multilingual corpus to learn versatile multilingual contextualized embeddings.