Over the last few years, large language models (LLMs) have emerged as the most important
breakthroughs in natural language processing (NLP) that fundamentally transform research …

随着深度学习的快速发展, 生成式模型领域也取得了显著进展. 生成对抗网络(Generative
adversarial network, GAN) 是一种无监督的学习方法, 它是根据博弈论中的二人零和博弈理论 …

State-of-the-art natural language processing systems rely on supervision in the form of
annotated data to learn competent models. These models are generally trained on data in a …

Recent work has managed to learn cross-lingual word embeddings without parallel data by
mapping monolingual embeddings to a shared space through adversarial training …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is
difficult to obtain for most language pairs. This has motivated an active research line to relax …

Cross-lingual or cross-domain correspondences play key roles in tasks ranging from
machine translation to transfer learning. Recently, purely unsupervised methods operating …

Using a dictionary to map independently trained word embeddings to a shared space has
shown to be an effective approach to learn bilingual word embeddings. In this work, we …

Word embeddings are well known to capture linguistic regularities of the language on which
they are trained. Researchers also observe that these regularities can transfer across …

For languages with no annotated resources, unsupervised transfer of natural language
processing models such as named-entity recognition (NER) from resource-rich languages …