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In-Person Poster presentation / top 5% paper

Encoding Recurrence into Transformers

Feiqing Huang · Kexin Lu · Yuxi Cai · Zhen Qin · Yanwen Fang · Guangjian Tian · Guodong Li

MH1-2-3-4 #79

Keywords: [ transformers ] [ sample efficiency ] [ gated mechanism ] [ Recurrent models ] [ Deep Learning and representational learning ]


Abstract:

This paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be encapsulated into the positional encodings of a multihead self-attention, and this makes it possible to seamlessly incorporate these recurrent dynamics into a Transformer, leading to a new module, Self-Attention with Recurrence (RSA). The proposed module can leverage the recurrent inductive bias of REMs to achieve a better sample efficiency than its corresponding baseline Transformer, while the self-attention is used to model the remaining non-recurrent signals. The relative proportions of these two components are controlled by a data-driven gated mechanism, and the effectiveness of RSA modules are demonstrated by four sequential learning tasks.

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