Word Alignment

Word alignment, the task of identifying corresponding words across different languages in parallel sentences, is crucial for various natural language processing applications, particularly machine translation and cross-lingual understanding. Current research emphasizes developing robust and efficient alignment models, particularly for low-resource languages, often employing techniques like contrastive learning, optimal transport, and transformer-based architectures including BiLSTMs and variations of the Transformer architecture. These advancements aim to improve alignment accuracy, address issues like hallucination and omission in machine translation, and facilitate downstream tasks such as cross-lingual sentence embedding and named entity recognition, ultimately enhancing cross-lingual NLP capabilities.