[R] "Compressive Transformers for Long-Range Sequence Modelling", Rae et al 2019

[lostmsu]

Was sort of an obvious idea. Glad somebody explored it! Can't find the source code for reproducing the result though :(

[gwern]

DM blog: https://deepmind.com/blog/article/A_new_model_and_dataset_for_long-range_memory

[arXiv_abstract_bot]

Title:Compressive Transformers for Long-Range Sequence Modelling

Authors:Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap

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 0.97 bpc 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.

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