Vision models outperform token embeddings for Chinese text processing

A new study published on arXiv explores an alternative input representation for transformer models, challenging the common practice of using discrete token embeddings. Researchers found that using full glyph images of Chinese characters, processed by a vision encoder, significantly outperformed traditional token embeddings. This vision-based approach achieved a 21% relative improvement in accuracy and converged in half the training epochs compared to the baseline token-based model. The study suggests that this advantage is specific to character-based writing systems like Chinese, as it did not transfer directly to English. AI

IMPACT Suggests alternative input modalities could improve transformer performance, particularly for character-based languages.

RANK_REASON Academic paper detailing a novel approach to text representation for transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• English
• residual neural network
• Standard Chinese
• transformers
• vision transformer

• paper
• model release

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