Compressive Transformers for Long-Range Sequence Modelling
Authors: Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap
Keywords: memory, language modeling, transformer, compression
TL;DR: Long-range transformer using a compressive memory, achieves sota in wikitext-103 and enwik8 LM benchmarks, release a new book-level LM benchmark PG-19.
Abstract: We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19.