  The dot product self-attention is known to be central and indispensable to
state-of-the-art Transformer models. But is it really required? This paper
investigates the true importance and contribution of the dot product-based
self-attention mechanism on the performance of Transformer models. Via
extensive experiments, we find that (1) random alignment matrices surprisingly
perform quite competitively and (2) learning attention weights from token-token
(query-key) interactions is useful but not that important after all. To this
end, we propose \textsc{Synthesizer}, a model that learns synthetic attention
weights without token-token interactions. In our experiments, we first show
that simple Synthesizers achieve highly competitive performance when compared
against vanilla Transformer models across a range of tasks, including machine
translation, language modeling, text generation and GLUE/SuperGLUE benchmarks.
When composed with dot product attention, we find that Synthesizers
consistently outperform Transformers. Moreover, we conduct additional
comparisons of Synthesizers against Dynamic Convolutions, showing that simple
Random Synthesizer is not only $60\%$ faster but also improves perplexity by a
relative $3.5\%$. Finally, we show that simple factorized Synthesizers can
outperform Linformers on encoding only tasks.
