Extending Memory for Language Modelling (2305.11462v1)

Abstract: Breakthroughs in deep learning and memory networks have made major advances in natural language understanding. Language is sequential and information carried through the sequence can be captured through memory networks. Learning the sequence is one of the key aspects in learning the language. However, memory networks are not capable of holding infinitely long sequences in their memories and are limited by various constraints such as the vanishing or exploding gradient problem. Therefore, natural language understanding models are affected when presented with long sequential text. We introduce Long Term Memory network (LTM) to learn from infinitely long sequences. LTM gives priority to the current inputs to allow it to have a high impact. LLMing is an important factor in natural language understanding. LTM was tested in LLMing, which requires long term memory. LTM is tested on Penn Tree bank dataset, Google Billion Word dataset and WikiText-2 dataset. We compare LTM with other LLMs which require long term memory.