Do Transformers Need Deep Long-Range Memory

Autor:	Rae, Jack W., Razavi, Ali
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Deep attention models have advanced the modelling of sequential data across many domains. For language modelling in particular, the Transformer-XL -- a Transformer augmented with a long-range memory of past activations -- has been shown to be state-of-the-art across a variety of well-studied benchmarks. The Transformer-XL incorporates a long-range memory at every layer of the network, which renders its state to be thousands of times larger than RNN predecessors. However it is unclear whether this is necessary. We perform a set of interventions to show that comparable performance can be obtained with 6X fewer long range memories and better performance can be obtained by limiting the range of attention in lower layers of the network. Comment: published at 58th Annual Meeting of the Association for Computational Linguistics. 6 pages, 4 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.03356 Zobrazit plný text záznamu View this record from Arxiv