Chinese Text Embeddings

Chinese text embeddings are numerical representations of words and phrases in the Chinese language, aiming to capture semantic meaning and relationships for various natural language processing (NLP) tasks. Current research focuses on improving embedding quality through techniques like multi-task learning, knowledge distillation, and contrastive learning, often incorporating features from both Pinyin (romanization) and Hanzi (characters) to enhance accuracy and address challenges like gender bias in name prediction and idiom understanding. These advancements are crucial for improving the performance of numerous NLP applications, including machine translation, sentiment analysis, and question answering, particularly in the context of the growing use of Chinese language data. The development of comprehensive benchmarks and large-scale datasets is also a key area of focus, facilitating the creation and evaluation of more robust and effective embedding models.

Papers