Complexity of Symbolic Representation in Working Memory of Transformer Correlates with the Complexity of a Task

Summary

This paper investigates how adding a working memory to Transformer models can improve their ability to translate text by allowing them to store and use important information during the translation process.

What's the problem?

Transformers are widely used in natural language processing, especially for tasks like translating languages. However, they do not have a built-in memory system to remember key concepts from the texts they process. This limitation can lead to less accurate translations because the model might forget important details while generating the output.

What's the solution?

The researchers added a symbolic working memory to the Transformer model's decoder, which allows the model to store important keywords and information from the text it is translating. This working memory helps the model keep track of relevant information, improving its predictions and overall translation quality. The study found that the keywords from the translated text are stored in this memory, and the variety of words and parts of speech stored correlates with how complex the text being translated is.

Why it matters?

This research is important because it shows that enhancing Transformer models with a working memory can significantly improve their performance in tasks like machine translation. By allowing these models to remember and use key information, we can create more accurate and reliable AI systems for translating languages, which is crucial for communication in our increasingly globalized world.