Less is More! A slim architecture for optimal language translation
Luca Herranz-Celotti Ermal Rrapaj
The softmax attention mechanism has emerged as a noteworthy development in
the field of Artificial Intelligence research, building on the successes of
Transformer-based architectures. However, their ever increasing sizes
necessitate ever increasing computational memory, that limits their usage. We
propose KgV, a sigmoid gating mechanism that, in conjunction with softmax
attention, significantly boosts performance without increasing architecture
size. To amend the size requirements, we leverage Tensor Chains to identify and
prune the excess parameters. We find that such excess resides primarily within
the embedding layer, and not in the output linear layer. To further improve
embedding and significantly reduce parameters, we introduce H-SoftPOS, a
hierarchical embedding layer which simultaneously enhances performance.
Remarkably, on the WMT14 English-German validation set, our approach yields a
threefold reduction in perplexity, surpassing the current state-of-the-art,
while reducing parameter counts also by a factor of 3. When we further reduce
the number of parameters up to sevenfold, we can still achieve a 21\% decrease
in perplexity with respect to the baseline Transformer. To understand
generalization capabilities, we conduct experiments on the 7 language pairs of
the WMT17 dataset. Our method outperforms existing techniques in terms of test
loss while simultaneously halving the number of parameters. Moreover, we
observe a 70 times reduction in variance with respect to the prior
state-of-the-art. In conclusion, our proposed method yields significant
improvements in performance and much lower memory cost. We call the resulting
architecture Anthe.