Bilingual Word Alignment

Bilingual word alignment aims to identify corresponding words across sentences in two different languages, a crucial step for various natural language processing tasks. Recent research focuses on improving alignment accuracy and efficiency using techniques like sentence embeddings, graph neural networks, and language-image pretraining, often addressing challenges posed by non-monotonic or fragmentary parallel text and low-resource languages. These advancements enable more robust and efficient cross-lingual transfer learning, impacting applications such as machine translation, cross-lingual annotation, and typological research. The development of methods that require no parallel corpora is a particularly active area of investigation.