Before revision:  Attention patterns over the memory suggest that it improves the model's ability to process a global context . We expect that the application of Memory Transformer architectures to the tasks of language modeling, reading comprehension, and text summarization, as well as other NLP tasks that require the processing of long contexts will contribute to solving challenging problems of natural language understanding and generation .
After revision:  Attention patterns over the memory suggest that it improves the model's ability to process a global context .